Methods and compositions for diagnosing and monitoring transplant rejection

ABSTRACT

Methods of diagnosing or monitoring transplant rejection, particularly cardiac transplant rejection, in a patient by detecting the expression level of one or more genes in a patient, are described. Diagnostic oligonucleotides for diagnosing or monitoring transplant rejection, particularly cardiac transplant rejection and kits or systems containing the same are also described.

RELATED APPLICATIONS

This application is a U.S. National Phase Application of InternationalApplication Serial No. PCT/US03/12946, filed Apr. 24, 2003, which claimspriority to U.S. patent application Ser. No. 10/131,831, filed Apr. 24,2002, and Ser. No. 10/325,899 filed Dec. 20, 2002, all of which arehereby incorporated by reference in their entirety.

FIELD OF THE INVENTION

This invention is in the field of expression profiling following organtransplantation.

BACKGROUND OF THE INVENTION

Many of the current shortcomings in diagnosis, prognosis, riskstratification and treatment of disease can be approached through theidentification of the molecular mechanisms underlying a disease andthrough the discovery of nucleotide sequences (or sets of nucleotidesequences) whose expression patterns predict the occurrence orprogression of disease states, or predict a patient's response to aparticular therapeutic intervention. In particular, identification ofnucleotide sequences and sets of nucleotide sequences with suchpredictive value from cells and tissues that are readily accessiblewould be extremely valuable. For example, peripheral blood is attainablefrom all patients and can easily be obtained at multiple time points atlow cost. This is a desirable contrast to most other cell and tissuetypes, which are less readily accessible, or accessible only throughinvasive and aversive procedures. In addition, the various cell typespresent in circulating blood are ideal for expression profilingexperiments as the many cell types in the blood specimen can be easilyseparated if desired prior to analysis of gene expression. While bloodprovides a very attractive substrate for the study of diseases usingexpression profiling techniques, and for the development of diagnostictechnologies and the identification of therapeutic targets, the value ofexpression profiling in blood samples rests on the degree to whichchanges in gene expression in these cell types are associated with apredisposition to, and pathogenesis and progression of a disease.

Hematopoiesis is the development and maturation of all cell types of theblood. These include erythrocytes, platelets and leukocytes. Leukocytesare further subdivided into granulocytes (neutrophils, eosinophils,basophils) and mononuclear cells (monocytes, lymphocytes). These cellsdevelop and mature from precursor cells to replenish the circulatingpool and to respond to insults and challenges to the system. This occursin the bone marrow, spleen, thymus, liver, lymph nodes, mucosalassociated lymphoid tissue (MALT) and peripheral blood.

Precursor cells differentiate into immature forms of each lineage andthese immature cells develop further into mature cells. This processoccurs under the influence and direction of hematopoietic growthfactors. When hematopoiesis is stimulated, there is an increase in thenumber of immature cells in the peripheral blood and in some cases,precursor cells are found at increased frequency. For example, CD34+cells (hematopoietic stem cells) may increase in frequency in theperipheral blood with an insult to the immune system. For neutrophils,“band” forms are increased, for erythrocytes, reticulocytes or nucleatedred cells are seen. Lymphocytes are preceeded by lymphoblasts (immaturelymphocytes).

It may be an important clinical goal to measure the rate of productionof blood cells of a variety of lineages. Hematological disordersinvolving over or under production of various blood cells may be treatedpharmacologically. For example, anemia (low red blood cells) may betreated with erythropoietin (a hematopoietic growth factor) and responseto this therapy can be assessed by measuring RBC production rates. Lowneutrophils counts can be treated by administration of G-CSF and thistherapy may be monitored by measuring neutrophil production rates.Alternatively, the diagnosis of blood cell disorders is greatlyfacilitated by determination of lineage specific production rates. Forexample, anemia (low RBCs) may be caused by decreased cellularproduction or increased destruction of cells. In the latter case, therate of cellular production will be increased rather than decreased andthe therapeutic implications are very different. Further discussion ofthe clinical uses of measures of blood cell production rates is given inbelow.

Assessment of blood cell production rates may be useful for diagnosisand management of non-hematological disorders. In particular, acuteallograft rejection diagnosis and monitoring may benefit from such anapproach. Current diagnosis and monitoring of acute allograft rejectionis achieved through invasive allograft biopsy and assessment of thebiopsy histology. This approach is sub-optimal because of expense of theprocedure, cost, pain and discomfort of the patient, the need fortrained physician operators, the risk of complications of the procedure,the lack of insight into the functioning of the immune system andvariability of pathological assessment. In addition, biopsy can diagnoseacute allograft rejection only after significant cellular infiltrationinto the allograft has occurred. At this point, the process has alreadycaused damage to the allograft. For all these reasons, a simple bloodtest that can diagnose and monitor acute rejection at an earlier stagein the process is needed. Allograft rejection depends on the presence offunctioning cells of the immune system. In addition, the process ofrejection may cause activation of hematopoiesis. Finally, effectiveimmunosuppressive therapy to treat or prevent acute rejection maysuppress hematopoiesis. For these reasons, assessment of hematopoieticcellular production rates may be useful in the diagnosis and monitoringof acute rejection.

Current techniques for measuring cellular development and productionrates are inadequate. The most common approach is to measure the numberof mature cells of a lineage of interest over time. For example, if apatient is being treated for anemia (low red blood cell counts), thenthe physician will order a blood cell count to assess the number of redblood cells (RBCs) in circulation. For this to be effective, thephysician must measure the cell count over time and may have to wait 2-4weeks before being able to assess response to therapy. The samelimitation is true for assessment of any cell lineage in the blood.

An alternative approach is to count the number of immature cells in theperipheral blood by counting them under the microscope. This may allow amore rapid assessment of cellular production rates, but is limited bythe need for assessment by a skilled hematologist, observer variabilityand the inability to distinguish all precursor cells on the basis ofmorphology alone.

Bone marrow biopsy is the gold standard for assessment of cellularproduction rates. In addition to the limitations of the need for skilledphysicians, reader variability and the lack of sensitivity of morphologyalone, the technique is also limited by the expense, discomfort to thepatient and need for a prolonged visit to a medical center. Thus thereis a need for a reliable, rapid means for measuring the rate ofhematopoeisis in a patient.

In addition to the relationship between hematopoiesis and variety ofdisease processes, there is an extensive literature supporting the roleof leukocytes, e.g., T- and B-lymphocytes, monocytes and granulocytes,including neutrophils, in a wide range of disease processes, includingsuch broad classes as cardiovascular diseases, inflammatory, autoimmuneand rheumatic diseases, infectious diseases, transplant rejection,cancer and malignancy, and endocrine diseases. For example, amongcardiovascular diseases, such commonly occurring diseases asatherosclerosis, restenosis, transplant vasculopathy and acute coronarysyndromes all demonstrate significant T cell involvement (Smith-Norowitzet al. (1999) Clin Immunol 93:168-175; Jude et al. (1994) Circulation90:1662-8; Belch et al. (1997) Circulation 95:2027-31). These diseasesare now recognized as manifestations of chronic inflammatory disordersresulting from an ongoing response to an injury process in the arterialtree (Ross et al. (1999) Ann Thorac Surg 67:1428-33). Differentialexpression of lymphocyte, monocyte and neutrophil genes and theirproducts has been demonstrated clearly in the literature. Particularlyinteresting are examples of differential expression in circulating cellsof the immune system that demonstrate specificity for a particulardisease, such as arteriosclerosis, as opposed to a generalizedassociation with other inflammatory diseases, or for example, withunstable angina rather than quiescent coronary disease.

A number of individual genes, e.g., CD11b/CD18 (Kassirer et al. (1999)Am Heart J 138:555-9); leukocyte elastase (Amaro et al. (1995) Eur HeartJ 16:615-22; and CD40L (Aukrust et al. (1999) Circulation 100:614-20)demonstrate some degree of sensitivity and specificity as markers ofvarious vascular diseases. In addition, the identification ofdifferentially expressed target and fingerprint genes isolated frompurified populations of monocytes manipulated in various in vitroparadigms has been proposed for the diagnosis and monitoring of a rangeof cardiovascular diseases, see, e.g., U.S. Pat. Nos. 6,048,709;6,087,477; 6,099,823; and 6,124,433 “COMPOSITIONS AND METHODS FOR THETREATMENT AND DIAGNOSIS OF CARDIOVASCULAR DISEASE” to Falb (see also, WO97/30065). Lockhart, in U.S. Pat. No. 6,033,860 “EXPRESSION PROFILES INADULT AND FETAL ORGANS” proposes the use of expression profiles for asubset of identified genes in the identification of tissue samples, andthe monitoring of drug effects.

The accuracy of technologies based on expression profiling for thediagnosis, prognosis, and monitoring of disease would be dramaticallyincreased if numerous differentially expressed nucleotide sequences,each with a measure of specificity for a disease in question, could beidentified and assayed in a concerted manner. PCT application WO02/057414 “LEUKOCYTE EXPRESSION PROFILING” to Wohlgemuth identifies onesuch set of differentially expressed nucleotides.

In order to achieve this improved accuracy, the sets of nucleotidesequences once identified need to be validated to identify thosedifferentially expressed nucleotides within a given set that are mostuseful for diagnosis, prognosis, and monitoring of disease. The presentinvention addresses these and other needs, and applies to transplantrejection and detection of the rate of hematopoeisis for whichdifferential regulation of genes, or other nucleotide sequences, ofperipheral blood can be demonstrated.

SUMMARY OF THE INVENTION

In order to meet these needs, the present invention is thus directed toa system for detecting differential gene expression. In one format,method are provided for assessing the immune status of an individual bydetecting the expression level of one or more genes expressed atdifferent levels depending upon the rate of hematopoiesis or thedistribution of hematopoietic cells along their maturation pathway inthe individual. The one or more genes may include a nucleotide selectedfrom a nucleotide sequence selected from SEQ ID NO:2, SEQ ID NO:3, SEQID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ IDNO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ IDNO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ IDNO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ IDNO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ IDNO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ IDNO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ IDNO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ IDNO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ IDNO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ IDNO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ ID NO:58, SEQ IDNO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ ID NO:63, SEQ IDNO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, 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NO:2676, SEQ ID NO:2701, SEQ ID NO:2730,SEQ ID NO:2710, SEQ ID NO:2632, SEQ ID NO:2724, SEQ ID NO:2698, SEQ IDNO:2662, SEQ ID NO:2753, SEQ ID NO:2704, SEQ ID NO:2675, SEQ ID NO:2700,SEQ ID NO:2640, SEQ ID NO:2723, SEQ ID NO:2658, SEQ ID NO:2688, SEQ IDNO:2735, SEQ ID NO:2702, SEQ ID NO:2681, SEQ ID NO:2755, SEQ ID NO:2715,SEQ ID NO:2732, SEQ ID NO:2652, SEQ ID NO:2651, SEQ ID NO:2718, SEQ IDNO:2673, SEQ ID NO:2733, SEQ ID NO:2712, SEQ ID NO:2659, SEQ ID NO:2654,SEQ ID NO:2636, SEQ ID NO:2639, SEQ ID NO:2690, SEQ ID NO:2705, SEQ IDNO:2685, SEQ ID NO:2692, SEQ ID NO:2693, SEQ ID NO:2648, SEQ ID NO:2650,SEQ ID NO:2720, SEQ ID NO:2660, SEQ ID NO:2666, SEQ ID NO:2699, SEQ IDNO:2633, SEQ ID NO:2672, SEQ ID NO:2642, SEQ ID NO:2682, SEQ ID NO:2655,SEQ ID NO:2630, SEQ ID NO:2745, SEQ ID NO:2643, SEQ ID NO:2694, SEQ IDNO:2749, SEQ ID NO:2665, SEQ ID NO:2649, SEQ ID NO:2637, SEQ ID NO:2634,SEQ ID NO:2709, SEQ ID NO:2653, SEQ ID NO:2729. The expression level maybe detected by measuring the RNA level expressed by the one or moregenes. In one variation, the RNA level is detected by PCR. In anothervariation, the RNA level is detected by hybridization. The expressionlevel may also be detected by measuring one or more proteins expressedby the one or more genes.

The present invention is further directed to methods of diagnosing ormonitoring transplant rejection in an individual by detecting a rate ofhematopoiesis. The detection may be applied directly to the individual,or to a sample isolated from the individual. Detection may beaccomplished by RNA profiling assay, immunoassay, fluorescent activatedcell sorting, protein assay, peripheral blood cytology assay, MRIimaging, bone marrow aspiration, and/or nuclear imaging. In onevariation, the RNA profile assay is a PCR based assay. In anothervariation, the RNA profile assay is a hybridization based assay. The RNAprofile assay may further include detecting the expression level of oneor more genes in the individual where the one or more genes include anucleotide sequence selected from SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4,SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ IDNO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ IDNO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ IDNO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ IDNO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ IDNO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ ID NO:34, SEQ IDNO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ IDNO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ IDNO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ IDNO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ IDNO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ ID NO:58, SEQ ID NO:59, SEQ IDNO:60, SEQ ID NO:61, 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SEQ ID NO:132, SEQID NO:133, SEQ ID NO:134, SEQ ID NO:135, SEQ ID NO:136, SEQ ID NO:137,SEQ ID NO:138, SEQ ID NO:139, SEQ ID NO:140, SEQ ID NO:141, SEQ IDNO:142, SEQ ID NO:143, SEQ ID NO:144, SEQ ID NO:145, SEQ ID NO:146, SEQID NO:147, SEQ ID NO:148, SEQ ID NO:149, SEQ ID NO:150, SEQ ID NO:151,SEQ ID NO:152, SEQ ID NO:153, SEQ ID NO:154, SEQ ID NO:155, SEQ IDNO:156, SEQ ID NO:157, SEQ ID NO:158, SEQ ID NO:159, SEQ ID NO:160, SEQID NO:161, SEQ ID NO:162, SEQ ID NO:163, SEQ ID NO:164, SEQ ID NO:165,SEQ ID NO:166, SEQ ID NO:167, SEQ ID NO:168, SEQ ID NO:169, SEQ IDNO:170, SEQ ID NO:171, SEQ ID NO:172, SEQ ID NO:173, SEQ ID NO:174, SEQID NO:175, SEQ ID NO:176, SEQ ID NO:177, SEQ ID NO:178, SEQ ID NO:179,SEQ ID NO:180, SEQ ID NO:181, SEQ ID NO:182, SEQ ID NO:183, SEQ IDNO:184, SEQ ID NO:185, SEQ ID NO:186, SEQ ID NO:187, SEQ ID NO:188, SEQID NO:189, SEQ ID NO:190, SEQ ID NO:191, SEQ ID NO:192, SEQ ID NO:193,SEQ ID NO:194, SEQ ID NO:195, SEQ ID NO:196, SEQ ID NO:197, SEQ IDNO:198, SEQ ID NO:199, SEQ ID NO:200, SEQ ID NO:201, SEQ ID NO:202, SEQID NO:203, SEQ ID NO:204, SEQ ID NO:205, SEQ ID NO:206, SEQ ID NO:207,SEQ ID NO:208, SEQ ID NO:209, SEQ ID NO:210, SEQ ID NO:211, SEQ IDNO:212, SEQ ID NO:213, SEQ ID NO:214, SEQ ID NO:215, SEQ ID NO:216, SEQID NO:217, SEQ ID NO:218, SEQ ID NO:219, SEQ ID NO:220, SEQ ID NO:221,SEQ ID NO:222, SEQ ID NO:223, SEQ ID NO:224, SEQ ID NO:225, SEQ IDNO:226, SEQ ID NO:227, SEQ ID NO:228, SEQ ID NO:229, SEQ ID NO:230, SEQID NO:231, SEQ ID NO:232, SEQ ID NO:233, SEQ ID NO:234, SEQ ID NO:235,SEQ ID NO:236, SEQ ID NO:237, SEQ ID NO:238, SEQ ID NO:239, SEQ IDNO:240, SEQ ID NO:241, SEQ ID NO:242, SEQ ID NO:243, SEQ ID NO:244, SEQID NO:245, SEQ ID NO:246, SEQ ID NO:247, SEQ ID NO:248, SEQ ID NO:249,SEQ ID NO:250, SEQ ID NO:251, SEQ ID NO:252, SEQ ID NO:253, SEQ IDNO:254, SEQ ID NO:255, SEQ ID NO:256, SEQ ID NO:257, SEQ ID NO:258, SEQID NO:259, SEQ ID NO:260, SEQ ID NO:261, SEQ ID NO:262, SEQ ID NO:263,SEQ ID NO:264, SEQ ID NO:265, SEQ ID NO:266, SEQ ID NO:267, SEQ IDNO:268, SEQ ID NO:269, SEQ ID NO:270, SEQ ID NO:271, SEQ ID NO:272, SEQID NO:273, SEQ ID NO:274, SEQ ID NO:275, SEQ ID NO:276, SEQ ID NO:277,SEQ ID NO:278, SEQ ID NO:279, SEQ ID NO:280, SEQ ID NO:281, SEQ IDNO:282, SEQ ID NO:283, SEQ ID NO:284, SEQ ID NO:285, SEQ ID NO:286, SEQID NO:287, SEQ ID NO:288, SEQ ID NO:289, SEQ ID NO:290, SEQ ID NO:291,SEQ ID NO:292, SEQ ID NO:293, SEQ ID NO:294, SEQ ID NO:295, SEQ IDNO:296, SEQ ID NO:297, SEQ ID NO:298, SEQ ID NO:299, SEQ ID NO:300, SEQID NO:301, SEQ ID NO:302, SEQ ID NO:303, SEQ ID NO:304, SEQ ID NO:305,SEQ ID NO:306, SEQ ID NO:307, SEQ ID NO:308, SEQ ID NO:309, SEQ IDNO:310, SEQ ID NO:311, SEQ ID NO:312, SEQ ID NO:313, SEQ ID NO:314, SEQID NO:315, SEQ ID NO:316, SEQ ID NO:317, SEQ ID NO:318, SEQ ID NO:319,SEQ ID NO:320, SEQ ID NO:321, SEQ ID NO:322, SEQ ID NO:323, SEQ IDNO:324, SEQ ID NO:325, SEQ ID NO:326, SEQ ID NO:327, SEQ ID NO:328, SEQID NO:329, SEQ ID NO:330, SEQ ID NO:331, SEQ ID NO:332, SEQ ID NO:2697,SEQ ID NO:2645, SEQ ID NO:2707, SEQ ID NO:2679, SEQ ID NO:2717, SEQ IDNO:2646, SEQ ID NO:2667, SEQ ID NO:2706, SEQ ID NO:2740, SEQ ID NO:2669,SEQ ID NO:2674, SEQ ID NO:2743, SEQ ID NO:2716, SEQ ID NO:2727, SEQ IDNO:2721, SEQ ID NO:2641, SEQ ID NO:2671, SEQ ID NO:2752, SEQ ID NO:2737,SEQ ID NO:2719, SEQ ID NO:2684, SEQ ID NO:2677, SEQ ID NO:2748, SEQ IDNO:2703, SEQ ID NO:2711, SEQ ID NO:2663, SEQ ID NO:2657, SEQ ID NO:2683,SEQ ID NO:2686, SEQ ID NO:2687, SEQ ID NO:2644, SEQ ID NO:2664, SEQ IDNO:2747, SEQ ID NO:2744, SEQ ID NO:2678, SEQ ID NO:2731, SEQ ID NO:2713,SEQ ID NO:2736, SEQ ID NO:2708, SEQ ID NO:2670, SEQ ID NO:2661, SEQ IDNO:2680, SEQ ID NO:2754, SEQ ID NO:2728, SEQ ID NO:2742, SEQ ID NO:2668,SEQ ID NO:2750, SEQ ID NO:2746, SEQ ID NO:2738, SEQ ID NO:2627, SEQ IDNO:2739, SEQ ID NO:2647, SEQ ID NO:2628, SEQ ID NO:2638, SEQ ID NO:2725,SEQ ID NO:2714, SEQ ID NO:2635, SEQ ID NO:2751, SEQ ID NO:2629, SEQ IDNO:2695, SEQ ID NO:2741, SEQ ID NO:2691, SEQ ID NO:2726, SEQ ID NO:2722,SEQ ID NO:2689, SEQ ID NO:2734, SEQ ID NO:2631, SEQ ID NO:2656, SEQ IDNO:2696, SEQ ID NO:2676, SEQ ID NO:2701, SEQ ID NO:2730, SEQ ID NO:2710,SEQ ID NO:2632, SEQ ID NO:2724, SEQ ID NO:2698, SEQ ID NO:2662, SEQ IDNO:2753, SEQ ID NO:2704, SEQ ID NO:2675, SEQ ID NO:2700, SEQ ID NO:2640,SEQ ID NO:2723, SEQ ID NO:2658, SEQ ID NO:2688, SEQ ID NO:2735, SEQ IDNO:2702, SEQ ID NO:2681, SEQ ID NO:2755, SEQ ID NO:2715, SEQ ID NO:2732,SEQ ID NO:2652, SEQ ID NO:2651, SEQ ID NO:2718, SEQ ID NO:2673, SEQ IDNO:2733, SEQ ID NO:2712, SEQ ID NO:2659, SEQ ID NO:2654, SEQ ID NO:2636,SEQ ID NO:2639, SEQ ID NO:2690, SEQ ID NO:2705, SEQ ID NO:2685, SEQ IDNO:2692, SEQ ID NO:2693, SEQ ID NO:2648, SEQ ID NO:2650, SEQ ID NO:2720,SEQ ID NO:2660, SEQ ID NO:2666, SEQ ID NO:2699, SEQ ID NO:2633, SEQ IDNO:2672, SEQ ID NO:2642, SEQ ID NO:2682, SEQ ID NO:2655, SEQ ID NO:2630,SEQ ID NO:2745, SEQ ID NO:2643, SEQ ID NO:2694, SEQ ID NO:2749, SEQ IDNO:2665, SEQ ID NO:2649, SEQ ID NO:2637, SEQ ID NO:2634, SEQ ID NO:2709,SEQ ID NO:2653, SEQ ID NO:2729. Transplant rejection may include one ormore of heart transplant rejection, kidney transplant rejection, livertransplant rejection, pancreas transplant rejection, pancreatic islettransplant rejection, lung transplant rejection, bone marrow transplantrejection, stem cell transplant rejection, xenotransplant rejection, andmechanical organ replacement rejection.

In another aspect, the invention is directed to a method of diagnosingor monitoring transplant rejection in a patient by detecting theexpression level of one or more genes in the patient to diagnose ormonitor transplant rejection in the patient, wherein the one or moregenes include a nucleotide sequence selected from SEQ ID NO:2, SEQ IDNO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:9,SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ ID NO:14,SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ ID NO:19,SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24,SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ ID NO:29,SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ ID NO:34,SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39,SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44,SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49,SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54,SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ ID NO:58, SEQ ID NO:59,SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ ID NO:63, SEQ ID NO:64,SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, SEQ ID NO:68, SEQ ID NO:69,SEQ ID NO:70, SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ ID NO:74,SEQ ID NO:82, SEQ ID NO:83, SEQ ID NO:84, SEQ ID NO:85, SEQ ID NO:86,SEQ ID NO:87, SEQ ID NO:88, SEQ ID NO:90, SEQ ID NO:91, SEQ ID NO:92,SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:98,SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ IDNO:105, SEQ ID NO:106, SEQ ID NO:107, SEQ ID NO:108, SEQ ID NO:109, SEQID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ ID NO:117, SEQ ID NO:118,SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQ ID NO:122, SEQ IDNO:123, SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132,SEQ ID NO:133, SEQ ID NO:134, SEQ ID NO:135, SEQ ID NO:136, SEQ IDNO:137, SEQ ID NO:138, SEQ ID NO:139, SEQ ID NO:152, SEQ ID NO:153, SEQID NO:154, SEQ ID NO:155, SEQ ID NO:156, SEQ ID NO:157, SEQ ID NO:158,SEQ ID NO:159, SEQ ID NO:160, SEQ ID NO:161, SEQ ID NO:162, SEQ IDNO:163, SEQ ID NO:164, SEQ ID NO:165, SEQ ID NO:166, SEQ ID NO:167, SEQID NO:168, SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171, SEQ ID NO:172,SEQ ID NO:173, SEQ ID NO:174, SEQ ID NO:175, SEQ ID NO:176, SEQ IDNO:177, SEQ ID NO:178, SEQ ID NO:179, SEQ ID NO:180, SEQ ID NO:181, SEQID NO:182, SEQ ID NO:183, SEQ ID NO:184, SEQ ID NO:185, SEQ ID NO:186,SEQ ID NO:187, SEQ ID NO:188, SEQ ID NO:189, SEQ ID NO:190, SEQ IDNO:191, SEQ ID NO:192, SEQ ID NO:193, SEQ ID NO:194, SEQ ID NO:195, SEQID NO:196, SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199, SEQ ID NO:200,SEQ ID NO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ ID NO:204, SEQ IDNO:205, SEQ ID NO:206, SEQ ID NO:207, SEQ ID NO:208, SEQ ID NO:209, SEQID NO:210, SEQ ID NO:211, SEQ ID NO:212, SEQ ID NO:213, SEQ ID NO:214,SEQ ID NO:215, SEQ ID NO:216, SEQ ID NO:217, SEQ ID NO:218, SEQ IDNO:219, SEQ ID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQ ID NO:223, SEQID NO:224, SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227, SEQ ID NO:228,SEQ ID NO:229, SEQ ID NO:230, SEQ ID NO:231, SEQ ID NO:232, SEQ IDNO:233, SEQ ID NO:234, SEQ ID NO:235, SEQ ID NO:236, SEQ ID NO:237, SEQID NO:238, SEQ ID NO:239, SEQ ID NO:240, SEQ ID NO:241, SEQ ID NO:242,SEQ ID NO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ ID NO:246, SEQ IDNO:247, SEQ ID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQ ID NO:251, SEQID NO:252, SEQ ID NO:253, SEQ ID NO:254, SEQ ID NO:255, SEQ ID NO:256,SEQ ID NO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ ID NO:260, SEQ IDNO:261, SEQ ID NO:262, SEQ ID NO:263, SEQ ID NO:264, SEQ ID NO:265, SEQID NO:266, SEQ ID NO:267, SEQ ID NO:268, SEQ ID NO:269, SEQ ID NO:270,SEQ ID NO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ ID NO:274, SEQ IDNO:275, SEQ ID NO:276, SEQ ID NO:277, SEQ ID NO:278, SEQ ID NO:279, SEQID NO:280, SEQ ID NO:281, SEQ ID NO:282, SEQ ID NO:283, SEQ ID NO:284,SEQ ID NO:285, SEQ ID NO:286, SEQ ID NO:287, SEQ ID NO:288, SEQ IDNO:289, SEQ ID NO:290, SEQ ID NO:291, SEQ ID NO:292, SEQ ID NO:293, SEQID NO:294, SEQ ID NO:295, SEQ ID NO:296, SEQ ID NO:297, SEQ ID NO:298,SEQ ID NO:299, SEQ ID NO:300, SEQ ID NO:301, SEQ ID NO:302, SEQ IDNO:303, SEQ ID NO:304, SEQ ID NO:305, SEQ ID NO:306, SEQ ID NO:307, SEQID NO:308, SEQ ID NO:309, SEQ ID NO:310, SEQ ID NO:311, SEQ ID NO:312,SEQ ID NO:313, SEQ ID NO:314, SEQ ID NO:315, SEQ ID NO:316, SEQ IDNO:317, SEQ ID NO:318, SEQ ID NO:319, SEQ ID NO:320, SEQ ID NO:321, SEQID NO:322, SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325, SEQ ID NO:326,SEQ ID NO:327, SEQ ID NO:328, SEQ ID NO:329, SEQ ID NO:330, SEQ IDNO:331, SEQ ID NO:332, SEQ ID NO:2697, SEQ ID NO:2645, SEQ ID NO:2707,SEQ ID NO:2679, SEQ ID NO:2717, SEQ ID NO:2646, SEQ ID NO:2667, SEQ IDNO:2706, SEQ ID NO:2740, SEQ ID NO:2669, SEQ ID NO:2674, SEQ ID NO:2743,SEQ ID NO:2716, SEQ ID NO:2727, SEQ ID NO:2721, SEQ ID NO:2641, SEQ IDNO:2671, SEQ ID NO:2752, SEQ ID NO:2737, SEQ ID NO:2719, SEQ ID NO:2684,SEQ ID NO:2677, SEQ ID NO:2748, SEQ ID NO:2703, SEQ ID NO:2711, SEQ IDNO:2663, SEQ ID NO:2657, SEQ ID NO:2683, SEQ ID NO:2686, SEQ ID NO:2687,SEQ ID NO:2644, SEQ ID NO:2664, SEQ ID NO:2747, SEQ ID NO:2744, SEQ IDNO:2678, SEQ ID NO:2731, SEQ ID NO:2713, SEQ ID NO:2736, SEQ ID NO:2708,SEQ ID NO:2670, SEQ ID NO:2661, SEQ ID NO:2680, SEQ ID NO:2754, SEQ IDNO:2728, SEQ ID NO:2742, SEQ ID NO:2668, SEQ ID NO:2750, SEQ ID NO:2746,SEQ ID NO:2738, SEQ ID NO:2627, SEQ ID NO:2739, SEQ ID NO:2647, SEQ IDNO:2628, SEQ ID NO:2638, SEQ ID NO:2725, SEQ ID NO:2714, SEQ ID NO:2635,SEQ ID NO:2751, SEQ ID NO:2629, SEQ ID NO:2695, SEQ ID NO:2741, SEQ IDNO:2691, SEQ ID NO:2726, SEQ ID NO:2722, SEQ ID NO:2689, SEQ ID NO:2734,SEQ ID NO:2631, SEQ ID NO:2656, SEQ ID NO:2696, SEQ ID NO:2676, SEQ IDNO:2701, SEQ ID NO:2730, SEQ ID NO:2710, SEQ ID NO:2632, SEQ ID NO:2724,SEQ ID NO:2698, SEQ ID NO:2662, SEQ ID NO:2753, SEQ ID NO:2704, SEQ IDNO:2675, SEQ ID NO:2700, SEQ ID NO:2640, SEQ ID NO:2723, SEQ ID NO:2658,SEQ ID NO:2688, SEQ ID NO:2735, SEQ ID NO:2702, SEQ ID NO:2681, SEQ IDNO:2755, SEQ ID NO:2715, SEQ ID NO:2732, SEQ ID NO:2652, SEQ ID NO:2651,SEQ ID NO:2718, SEQ ID NO:2673, SEQ ID NO:2733, SEQ ID NO:2712, SEQ IDNO:2659, SEQ ID NO:2654, SEQ ID NO:2636, SEQ ID NO:2639, SEQ ID NO:2690,SEQ ID NO:2705, SEQ ID NO:2685, SEQ ID NO:2692, SEQ ID NO:2693, SEQ IDNO:2648, SEQ ID NO:2650, SEQ ID NO:2720, SEQ ID NO:2660, SEQ ID NO:2666,SEQ ID NO:2699, SEQ ID NO:2633, SEQ ID NO:2672, SEQ ID NO:2642, SEQ IDNO:2682, SEQ ID NO:2655, SEQ ID NO:2630, SEQ ID NO:2745, SEQ ID NO:2643,SEQ ID NO:2694, SEQ ID NO:2749, SEQ ID NO:2665, SEQ ID NO:2649, SEQ IDNO:2637, SEQ ID NO:2634, SEQ ID NO:2709, SEQ ID NO:2653, SEQ ID NO:2729.In one variation, the invention is further directed to detecting theexpression level of one or more additional genes in the patient todiagnose or monitor transplant rejection in the patient, wherein the oneor more additional genes include a nucleotide sequence selected from SEQID NO:8, SEQ ID NO:75, SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ IDNO:79, SEQ ID NO:80, SEQ ID NO:81, SEQ ID NO:89, SEQ ID NO:97, SEQ IDNO:99, SEQ ID NO:100, SEQ ID NO:110, SEQ ID NO:111, SEQ ID NO:112, SEQID NO:113, SEQ ID NO:140, SEQ ID NO:141, SEQ ID NO:142, SEQ ID NO:143,SEQ ID NO:144, SEQ ID NO:145, SEQ ID NO:146, SEQ ID NO:147, SEQ IDNO:148, SEQ ID NO:149, SEQ ID NO:150, SEQ ID NO:15.

In a further variation, the invention is directed to a method ofdiagnosing or monitoring cardiac transplant rejection in a patient bydetecting the expression level of one or more genes in the patient todiagnose or monitor cardiac transplant rejection in the patient whereinthe one or more genes include a nucleotide sequence selected from SEQ IDNO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7,SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ IDNO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ IDNO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ IDNO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ IDNO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ IDNO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ IDNO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ IDNO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ IDNO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ ID NO:58, SEQ IDNO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ ID NO:63, SEQ IDNO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, SEQ ID NO:68, SEQ IDNO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ IDNO:74, SEQ ID NO:75, SEQ ID NO:80, SEQ ID NO:81, SEQ ID NO:82, SEQ IDNO:83, SEQ ID NO:84, SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87, SEQ IDNO:88, SEQ ID NO:89, SEQ ID NO:90, SEQ ID NO:91, SEQ ID NO:92, SEQ IDNO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:98, SEQ IDNO:99, SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, SEQ ID NO:108,SEQ ID NO:109, SEQ ID NO:110, SEQ ID NO:111, SEQ ID NO:112, SEQ IDNO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ ID NO:117, SEQID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQ ID NO:122,SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQ IDNO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQ ID NO:135, SEQ ID NO:136,SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139, SEQ ID NO:152, SEQ IDNO:153, SEQ ID NO:154, SEQ ID NO:155, SEQ ID NO:156, SEQ ID NO:157, SEQID NO:158, SEQ ID NO:159, SEQ ID NO:160, SEQ ID NO:161, SEQ ID NO:162,SEQ ID NO:163, SEQ ID NO:164, SEQ ID NO:165, SEQ ID NO:166, SEQ IDNO:167, SEQ ID NO:168, SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171, SEQID NO:172, SEQ ID NO:173, SEQ ID NO:174, SEQ ID NO:175, SEQ ID NO:176,SEQ ID NO:177, SEQ ID NO:178, SEQ ID NO:179, SEQ ID NO:180, SEQ IDNO:181, SEQ ID NO:182, SEQ ID NO:183, SEQ ID NO:184, SEQ ID NO:185, SEQID NO:186, SEQ ID NO:187, SEQ ID NO:188, SEQ ID NO:189, SEQ ID NO:190,SEQ ID NO:191, SEQ ID NO:192, SEQ ID NO:193, SEQ ID NO:194, SEQ IDNO:195, SEQ ID NO:196, SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199, SEQID NO:200, SEQ ID NO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ ID NO:204,SEQ ID NO:205, SEQ ID NO:206, SEQ ID NO:207, SEQ ID NO:208, SEQ IDNO:209, SEQ ID NO:210, SEQ ID NO:211, SEQ ID NO:212, SEQ ID NO:213, SEQID NO:214, SEQ ID NO:215, SEQ ID NO:216, SEQ ID NO:217, SEQ ID NO:218,SEQ ID NO:219, SEQ ID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQ IDNO:223, SEQ ID NO:224, SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227, SEQID NO:228, SEQ ID NO:229, SEQ ID NO:230, SEQ ID NO:231, SEQ ID NO:232,SEQ ID NO:233, SEQ ID NO:234, SEQ ID NO:235, SEQ ID NO:236, SEQ IDNO:237, SEQ ID NO:238, SEQ ID NO:239, SEQ ID NO:240, SEQ ID NO:241, SEQID NO:242, SEQ ID NO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ ID NO:246,SEQ ID NO:247, SEQ ID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQ IDNO:251, SEQ ID NO:252, SEQ ID NO:253, SEQ ID NO:254, SEQ ID NO:255, SEQID NO:256, SEQ ID NO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ ID NO:260,SEQ ID NO:261, SEQ ID NO:262, SEQ ID NO:263, SEQ ID NO:264, SEQ IDNO:265, SEQ ID NO:266, SEQ ID NO:267, SEQ ID NO:268, SEQ ID NO:269, SEQID NO:270, SEQ ID NO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ ID NO:274,SEQ ID NO:275, SEQ ID NO:276, SEQ ID NO:277, SEQ ID NO:278, SEQ IDNO:279, SEQ ID NO:280, SEQ ID NO:281, SEQ ID NO:282, SEQ ID NO:283, SEQID NO:284, SEQ ID NO:285, SEQ ID NO:286, SEQ ID NO:287, SEQ ID NO:288,SEQ ID NO:289, SEQ ID NO:290, SEQ ID NO:291, SEQ ID NO:292, SEQ IDNO:293, SEQ ID NO:294, SEQ ID NO:295, SEQ ID NO:296, SEQ ID NO:297, SEQID NO:298, SEQ ID NO:299, SEQ ID NO:300, SEQ ID NO:301, SEQ ID NO:302,SEQ ID NO:303, SEQ ID NO:304, SEQ ID NO:305, SEQ ID NO:306, SEQ IDNO:307, SEQ ID NO:308, SEQ ID NO:309, SEQ ID NO:310, SEQ ID NO:311, SEQID NO:312, SEQ ID NO:313, SEQ ID NO:314, SEQ ID NO:315, SEQ ID NO:316,SEQ ID NO:317, SEQ ID NO:318, SEQ ID NO:319, SEQ ID NO:320, SEQ IDNO:321, SEQ ID NO:322, SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325, SEQID NO:326, SEQ ID NO:327, SEQ ID NO:328, SEQ ID NO:329, SEQ ID NO:330,SEQ ID NO:331, SEQ ID NO:332. In one variation, the method includesdetecting the expression level of one or more additional genes in thepatient to diagnose or monitor cardiac transplant rejection in thepatient, wherein the one or more additional genes include a nucleotidesequence selected from SEQ ID NO:8, SEQ ID NO:76, SEQ ID NO:77, SEQ IDNO:78, SEQ ID NO:79, SEQ ID NO:97, SEQ ID NO:140, SEQ ID NO:141, SEQ IDNO:142, SEQ ID NO:143, SEQ ID NO:144, SEQ ID NO:145, SEQ ID NO:146, SEQID NO:147, SEQ ID NO:148, SEQ ID NO:149, SEQ ID NO:150, SEQ ID NO:151.

The invention is also directed to a method of diagnosing or monitoringkidney transplant rejection in a patient by detecting the expressionlevel of one or more genes in the patient to diagnose or monitor kidneytransplant rejection in the patient wherein the one or more genesinclude a nucleotide sequence selected from SEQ ID NO:2, SEQ ID NO:3,SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ IDNO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ IDNO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ IDNO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ IDNO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ IDNO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ IDNO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ IDNO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ IDNO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ IDNO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ IDNO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ ID NO:58, SEQ IDNO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ ID NO:63, SEQ IDNO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, SEQ ID NO:68, SEQ IDNO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ IDNO:74, SEQ ID NO:78, SEQ ID NO:82, SEQ ID NO:83, SEQ ID NO:84, SEQ IDNO:85, SEQ ID NO:86, SEQ ID NO:87, SEQ ID NO:88, SEQ ID NO:90, SEQ IDNO:91, SEQ ID NO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ IDNO:96, SEQ ID NO:97, SEQ ID NO:98, SEQ ID NO:101, SEQ ID NO:102, SEQ IDNO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, SEQID NO:108, SEQ ID NO:109, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116,SEQ ID NO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ IDNO:121, SEQ ID NO:122, SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125, SEQID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130,SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQ IDNO:135, SEQ ID NO:136, SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139, SEQID NO:152, SEQ ID NO:153, SEQ ID NO:154, SEQ ID NO:155, SEQ ID NO:156,SEQ ID NO:157, SEQ ID NO:158, SEQ ID NO:159, SEQ ID NO:160, SEQ IDNO:161, SEQ ID NO:162, SEQ ID NO:163, SEQ ID NO:164, SEQ ID NO:165, SEQID NO:166, SEQ ID NO:167, SEQ ID NO:168, SEQ ID NO:169, SEQ ID NO:170,SEQ ID NO:171, SEQ ID NO:172, SEQ ID NO:173, SEQ ID NO:174, SEQ IDNO:175, SEQ ID NO:176, SEQ ID NO:177, SEQ ID NO:178, SEQ ID NO:179, SEQID NO:180, SEQ ID NO:181, SEQ ID NO:182, SEQ ID NO:183, SEQ ID NO:184,SEQ ID NO:185, SEQ ID NO:186, SEQ ID NO:187, SEQ ID NO:188, SEQ IDNO:189, SEQ ID NO:190, SEQ ID NO:191, SEQ ID NO:192, SEQ ID NO:193, SEQID NO:194, SEQ ID NO:195, SEQ ID NO:196, SEQ ID NO:197, SEQ ID NO:198,SEQ ID NO:199, SEQ ID NO:200, SEQ ID NO:201, SEQ ID NO:202, SEQ IDNO:203, SEQ ID NO:204, SEQ ID NO:205, SEQ ID NO:206, SEQ ID NO:207, SEQID NO:208, SEQ ID NO:209, SEQ ID NO:210, SEQ ID NO:211, SEQ ID NO:212,SEQ ID NO:213, SEQ ID NO:214, SEQ ID NO:215, SEQ ID NO:216, SEQ IDNO:217, SEQ ID NO:218, SEQ ID NO:219, SEQ ID NO:220, SEQ ID NO:221, SEQID NO:222, SEQ ID NO:223, SEQ ID NO:224, SEQ ID NO:225, SEQ ID NO:226,SEQ ID NO:227, SEQ ID NO:228, SEQ ID NO:229, SEQ ID NO:230, SEQ IDNO:231, SEQ ID NO:232, SEQ ID NO:233, SEQ ID NO:234, SEQ ID NO:235, SEQID NO:236, SEQ ID NO:237, SEQ ID NO:238, SEQ ID NO:239, SEQ ID NO:240,SEQ ID NO:241, SEQ ID NO:242, SEQ ID NO:243, SEQ ID NO:244, SEQ IDNO:245, SEQ ID NO:246, SEQ ID NO:247, SEQ ID NO:248, SEQ ID NO:249, SEQID NO:250, SEQ ID NO:251, SEQ ID NO:252, SEQ ID NO:253, SEQ ID NO:254,SEQ ID NO:255, SEQ ID NO:256, SEQ ID NO:257, SEQ ID NO:258, SEQ IDNO:259, SEQ ID NO:260, SEQ ID NO:261, SEQ ID NO:262, SEQ ID NO:263, SEQID NO:264, SEQ ID NO:265, SEQ ID NO:266, SEQ ID NO:267, SEQ ID NO:268,SEQ ID NO:269, SEQ ID NO:270, SEQ ID NO:271, SEQ ID NO:272, SEQ IDNO:273, SEQ ID NO:274, SEQ ID NO:275, SEQ ID NO:276, SEQ ID NO:277, SEQID NO:278, SEQ ID NO:279, SEQ ID NO:280, SEQ ID NO:281, SEQ ID NO:282,SEQ ID NO:283, SEQ ID NO:284, SEQ ID NO:285, SEQ ID NO:286, SEQ IDNO:287, SEQ ID NO:288, SEQ ID NO:289, SEQ ID NO:290, SEQ ID NO:291, SEQID NO:292, SEQ ID NO:293, SEQ ID NO:294, SEQ ID NO:295, SEQ ID NO:296,SEQ ID NO:297, SEQ ID NO:298, SEQ ID NO:299, SEQ ID NO:300, SEQ IDNO:301, SEQ ID NO:302, SEQ ID NO:303, SEQ ID NO:304, SEQ ID NO:305, SEQID NO:306, SEQ ID NO:307, SEQ ID NO:308, SEQ ID NO:309, SEQ ID NO:310,SEQ ID NO:311, SEQ ID NO:312, SEQ ID NO:313, SEQ ID NO:314, SEQ IDNO:315, SEQ ID NO:316, SEQ ID NO:317, SEQ ID NO:318, SEQ ID NO:319, SEQID NO:320, SEQ ID NO:321, SEQ ID NO:322, SEQ ID NO:323, SEQ ID NO:324,SEQ ID NO:325, SEQ ID NO:326, SEQ ID NO:327, SEQ ID NO:328, SEQ IDNO:329, SEQ ID NO:330, SEQ ID NO:331, SEQ ID NO:332, SEQ ID NO:2697, SEQID NO:2645, SEQ ID NO:2707, SEQ ID NO:2679, SEQ ID NO:2717, SEQ IDNO:2646, SEQ ID NO:2667, SEQ ID NO:2706, SEQ ID NO:2740, SEQ ID NO:2669,SEQ ID NO:2674, SEQ ID NO:2743, SEQ ID NO:2716, SEQ ID NO:2727, SEQ IDNO:2721, SEQ ID NO:2641, SEQ ID NO:2671, SEQ ID NO:2752, SEQ ID NO:2737,SEQ ID NO:2719, SEQ ID NO:2684, SEQ ID NO:2677, SEQ ID NO:2748, SEQ IDNO:2703, SEQ ID NO:2711, SEQ ID NO:2663, SEQ ID NO:2657, SEQ ID NO:2683,SEQ ID NO:2686, SEQ ID NO:2687, SEQ ID NO:2644, SEQ ID NO:2664, SEQ IDNO:2747, SEQ ID NO:2744, SEQ ID NO:2678, SEQ ID NO:2731, SEQ ID NO:2713,SEQ ID NO:2736, SEQ ID NO:2708, SEQ ID NO:2670, SEQ ID NO:2661, SEQ IDNO:2680, SEQ ID NO:2754, SEQ ID NO:2728, SEQ ID NO:2742, SEQ ID NO:2668,SEQ ID NO:2750, SEQ ID NO:2746, SEQ ID NO:2738, SEQ ID NO:2627, SEQ IDNO:2739, SEQ ID NO:2647, SEQ ID NO:2628, SEQ ID NO:2638, SEQ ID NO:2725,SEQ ID NO:2714, SEQ ID NO:2635, SEQ ID NO:2751, SEQ ID NO:2629, SEQ IDNO:2695, SEQ ID NO:2741, SEQ ID NO:2691, SEQ ID NO:2726, SEQ ID NO:2722,SEQ ID NO:2689, SEQ ID NO:2734, SEQ ID NO:2631, SEQ ID NO:2656, SEQ IDNO:2696, SEQ ID NO:2676, SEQ ID NO:2701, SEQ ID NO:2730, SEQ ID NO:2710,SEQ ID NO:2632, SEQ ID NO:2724, SEQ ID NO:2698, SEQ ID NO:2662, SEQ IDNO:2753, SEQ ID NO:2704, SEQ ID NO:2675, SEQ ID NO:2700, SEQ ID NO:2640,SEQ ID NO:2723, SEQ ID NO:2658, SEQ ID NO:2688, SEQ ID NO:2735, SEQ IDNO:2702, SEQ ID NO:2681, SEQ ID NO:2755, SEQ ID NO:2715, SEQ ID NO:2732,SEQ ID NO:2652, SEQ ID NO:2651, SEQ ID NO:2718, SEQ ID NO:2673, SEQ IDNO:2733, SEQ ID NO:2712, SEQ ID NO:2659, SEQ ID NO:2654, SEQ ID NO:2636,SEQ ID NO:2639, SEQ ID NO:2690, SEQ ID NO:2705, SEQ ID NO:2685, SEQ IDNO:2692, SEQ ID NO:2693, SEQ ID NO:2648, SEQ ID NO:2650, SEQ ID NO:2720,SEQ ID NO:2660, SEQ ID NO:2666, SEQ ID NO:2699, SEQ ID NO:2633, SEQ IDNO:2672, SEQ ID NO:2642, SEQ ID NO:2682, SEQ ID NO:2655, SEQ ID NO:2630,SEQ ID NO:2745, SEQ ID NO:2643, SEQ ID NO:2694, SEQ ID NO:2749, SEQ IDNO:2665, SEQ ID NO:2649, SEQ ID NO:2637, SEQ ID NO:2634, SEQ ID NO:2709,SEQ ID NO:2653, SEQ ID NO:2729. In one variation, the method furtherincludes detecting the expression level of one or more additional genesin the patient to diagnose or monitor kidney transplant rejection in apatient, wherein the one or more additional genes includes a nucleotidesequence selected from SEQ ID NO: 75, SEQ ID NO:76, SEQ ID NO:77, SEQ IDNO:79, SEQ ID NO:80, SEQ ID NO:81, SEQ ID NO:89, SEQ ID NO:99, SEQ IDNO:100, SEQ ID NO:110, SEQ ID NO:111, SEQ ID NO:112, SEQ ID NO:113, SEQID NO:140, SEQ ID NO:141, SEQ ID NO:142, SEQ ID NO:143, SEQ ID NO:144,SEQ ID NO:145, SEQ ID NO:146, SEQ ID NO:147, SEQ ID NO:148, SEQ IDNO:149, SEQ ID NO:150, SEQ ID NO:151.

In another aspect, the methods of diagnosing or monitoring transplantrejection include detecting the expression level of at least two of thegenes. In another variation, methods of diagnosing or monitoringtransplant rejection include detecting the expression level of at leastten of the genes. In a further variation, the methods of diagnosing ormonitoring transplant rejection include detecting the expression levelof at least one hundred of the genes. In still a further variation, themethods of diagnosing or monitoring transplant rejection includedetecting the expression level of all the listed genes.

In another variation, transplant rejection may be selected from hearttransplant rejection, kidney transplant rejection, liver transplantrejection, pancreas transplant rejection, pancreatic islet transplantrejection, lung transplant rejection, bone marrow transplant rejection,stem cell transplant rejection, xenotransplant rejection, and mechanicalorgan replacement rejection.

In another aspect, the methods of detecting transplant rejection includedetecting the expression level by measuring the RNA level expressed byone or more genes. The method may further including isolating RNA fromthe patient prior to detecting the RNA level expressed by the one ormore genes.

In one variation, the RNA level is detected by PCR. In a still furthervariation, the PCR uses primers consisting of nucleotide sequencesselected from the group consisting of SEQ ID NO:665, SEQ ID NO:666, SEQID NO:667, SEQ ID NO:668, SEQ ID NO:669, SEQ ID NO:670, SEQ ID NO:671,SEQ ID NO:672, SEQ ID NO:673, SEQ ID NO:674, SEQ ID NO:675, SEQ IDNO:676, SEQ ID NO:677, SEQ ID NO:678, SEQ ID NO:679, SEQ ID NO:680, SEQID NO:681, SEQ ID NO:682, SEQ ID NO:683, SEQ ID NO:684, SEQ ID NO:685,SEQ ID NO:686, SEQ ID NO:687, SEQ ID NO:688, SEQ ID NO:689, SEQ IDNO:690, SEQ ID NO:691, SEQ ID NO:692, SEQ ID NO:693, SEQ ID NO:694, SEQID NO:695, SEQ ID NO:696, SEQ ID NO:697, SEQ ID NO:698, SEQ ID NO:699,SEQ ID NO:700, SEQ ID NO:701, SEQ ID NO:702, SEQ ID NO:703, SEQ IDNO:704, SEQ ID NO:705, SEQ ID NO:706, SEQ ID NO:707, SEQ ID NO:708, SEQID NO:709, SEQ ID NO:710, SEQ ID NO:711, SEQ ID NO:712, SEQ ID NO:713,SEQ ID NO:714, SEQ ID NO:715, SEQ ID NO:716, SEQ ID NO:717, SEQ IDNO:718, SEQ ID NO:719, SEQ ID NO:720, SEQ ID NO:721, SEQ ID NO:722, SEQID NO:723, SEQ ID NO:724, SEQ ID NO:725, SEQ ID NO:726, SEQ ID NO:727,SEQ ID NO:728, SEQ ID NO:729, SEQ ID NO:730, SEQ ID NO:731, SEQ IDNO:732, SEQ ID NO:733, SEQ ID NO:734, SEQ ID NO:735, SEQ ID NO:736, SEQID NO:737, SEQ ID NO:738, SEQ ID NO:739, SEQ ID NO:740, SEQ ID NO:741,SEQ ID NO:742, SEQ ID NO:743, SEQ ID NO:744, SEQ ID NO:745, SEQ IDNO:746, SEQ ID NO:747, SEQ ID NO:748, SEQ ID NO:749, SEQ ID NO:750, SEQID NO:751, SEQ ID NO:752, SEQ ID NO:753, SEQ ID NO:754, SEQ ID NO:755,SEQ ID NO:756, SEQ ID NO:757, SEQ ID NO:758, SEQ ID NO:759, SEQ IDNO:760, SEQ ID NO:761, SEQ ID NO:762, SEQ ID NO:763, SEQ ID NO:764, SEQID NO:765, SEQ ID NO:766, SEQ ID NO:767, SEQ ID NO:768, SEQ ID NO:769,SEQ ID NO:770, SEQ ID NO:771, SEQ ID NO:772, SEQ ID NO:773, SEQ IDNO:774, SEQ ID NO:775, SEQ ID NO:776, SEQ ID NO:777, SEQ ID NO:778, SEQID NO:779, SEQ ID NO:780, SEQ ID NO:781, SEQ ID NO:782, SEQ ID NO:783,SEQ ID NO:784, SEQ ID NO:785, SEQ ID NO:786, SEQ ID NO:787, SEQ IDNO:788, SEQ ID NO:789, SEQ ID NO:790, SEQ ID NO:791, SEQ ID NO:792, SEQID NO:793, SEQ ID NO:794, SEQ ID NO:795, SEQ ID NO:796, SEQ ID NO:797,SEQ ID NO:798, SEQ ID NO:799, SEQ ID NO:800, SEQ ID NO:801, SEQ IDNO:802, SEQ ID NO:803, SEQ ID NO:804, SEQ ID NO:805, SEQ ID NO:806, SEQID NO:807, SEQ ID NO:808, SEQ ID NO:809, SEQ ID NO:810, SEQ ID NO:811,SEQ ID NO:812, SEQ ID NO:813, SEQ ID NO:814, SEQ ID NO:815, SEQ IDNO:816, SEQ ID NO:817, SEQ ID NO:818, SEQ ID NO:819, SEQ ID NO:820, SEQID NO:821, SEQ ID NO:822, SEQ ID NO:823, SEQ ID NO:824, SEQ ID NO:825,SEQ ID NO:826, SEQ ID NO:827, SEQ ID NO:828, SEQ ID NO:829, SEQ IDNO:830, SEQ ID NO:831, SEQ ID NO:832, SEQ ID NO:833, SEQ ID NO:834, SEQID NO:835, SEQ ID NO:836, SEQ ID NO:837, SEQ ID NO:838, SEQ ID NO:839,SEQ ID NO:840, SEQ ID NO:841, SEQ ID NO:842, SEQ ID NO:843, SEQ IDNO:844, SEQ ID NO:845, SEQ ID NO:846, SEQ ID NO:847, SEQ ID NO:848, SEQID NO:849, SEQ ID NO:850, SEQ ID NO:851, SEQ ID NO:852, SEQ ID NO:853,SEQ ID NO:854, SEQ ID NO:855, SEQ ID NO:856, SEQ ID NO:857, SEQ IDNO:858, SEQ ID NO:859, SEQ ID NO:860, SEQ ID NO:861, SEQ ID NO:862, SEQID NO:863, SEQ ID NO:864, SEQ ID NO:865, SEQ ID NO:866, SEQ ID NO:867,SEQ ID NO:868, SEQ ID NO:869, SEQ ID NO:870, SEQ ID NO:871, SEQ IDNO:872, SEQ ID NO:873, SEQ ID NO:874, SEQ ID NO:875, SEQ ID NO:876, SEQID NO:877, SEQ ID NO:878, SEQ ID NO:879, SEQ ID NO:880, SEQ ID NO:881,SEQ ID NO:882, SEQ ID NO:883, SEQ ID NO:884, SEQ ID NO:885, SEQ IDNO:886, SEQ ID NO:887, SEQ ID NO:888, SEQ ID NO:889, SEQ ID NO:890, SEQID NO:891, SEQ ID NO:892, SEQ ID NO:893, SEQ ID NO:894, SEQ ID NO:895,SEQ ID NO:896, SEQ ID NO:897, SEQ ID NO:898, SEQ ID NO:899, SEQ IDNO:900, SEQ ID NO:901, SEQ ID NO:902, SEQ ID NO:903, SEQ ID NO:904, SEQID NO:905, SEQ ID NO:906, SEQ ID NO:907, SEQ ID NO:908, SEQ ID NO:909,SEQ ID NO:910, SEQ ID NO:911, SEQ ID NO:912, SEQ ID NO:913, SEQ IDNO:914, SEQ ID NO:915, SEQ ID NO:916, SEQ ID NO:917, SEQ ID NO:918, SEQID NO:919, SEQ ID NO:920, SEQ ID NO:921, SEQ ID NO:922, SEQ ID NO:923,SEQ ID NO:924, SEQ ID NO:925, SEQ ID NO:926, SEQ ID NO:927, SEQ IDNO:928, SEQ ID NO:929, SEQ ID NO:930, SEQ ID NO:931, SEQ ID NO:932, SEQID NO:933, SEQ ID NO:934, SEQ ID NO:935, SEQ ID NO:936, SEQ ID NO:937,SEQ ID NO:938, SEQ ID NO:939, SEQ ID NO:940, SEQ ID NO:941, SEQ IDNO:942, SEQ ID NO:943, SEQ ID NO:944, SEQ ID NO:945, SEQ ID NO:946, SEQID NO:947, SEQ ID NO:948, SEQ ID NO:949, SEQ ID NO:950, SEQ ID NO:951,SEQ ID NO:952, SEQ ID NO:953, SEQ ID NO:954, SEQ ID NO:955, SEQ IDNO:956, SEQ ID NO:957, SEQ ID NO:958, SEQ ID NO:959, SEQ ID NO:960, SEQID NO:961, SEQ ID NO:962, SEQ ID NO:963, SEQ ID NO:964, SEQ ID NO:965,SEQ ID NO:966, SEQ ID NO:967, SEQ ID NO:968, SEQ ID NO:969, SEQ IDNO:970, SEQ ID NO:971, SEQ ID NO:972, SEQ ID NO:973, SEQ ID NO:974, SEQID NO:975, SEQ ID NO:976, SEQ ID NO:977, SEQ ID NO:978, SEQ ID NO:979,SEQ ID NO:980, SEQ ID NO:981, SEQ ID NO:982, SEQ ID NO:983, SEQ IDNO:984, SEQ ID NO:985, SEQ ID NO:986, SEQ ID NO:987, SEQ ID NO:988, SEQID NO:989, SEQ ID NO:990, SEQ ID NO:991, SEQ ID NO:992, SEQ ID NO:993,SEQ ID NO:994, SEQ ID NO:995, SEQ ID NO:996, SEQ ID NO:997, SEQ IDNO:998, SEQ ID NO:999, SEQ ID NO:1000, SEQ ID NO:1001, SEQ ID NO:1002,SEQ ID NO:1003, SEQ ID NO:1004, SEQ ID NO:1005, SEQ ID NO:1006, SEQ IDNO:1007, SEQ ID NO:1008, SEQ ID NO:1009, SEQ ID NO:1010, SEQ ID NO:1011,SEQ ID NO:1012, SEQ ID NO:1013, SEQ ID NO:1014, SEQ ID NO:1015, SEQ IDNO:1016, SEQ ID NO:1017, SEQ ID NO:1018, SEQ ID NO:1019, SEQ ID NO:1020,SEQ ID NO:1021, SEQ ID NO:1022, SEQ ID NO:1023, SEQ ID NO:1024, SEQ IDNO:1025, SEQ ID NO:1026, SEQ ID NO:1027, SEQ ID NO:1028, SEQ ID NO:1029,SEQ ID NO:1030, SEQ ID NO:1031, SEQ ID NO:1032, SEQ ID NO:1033, SEQ IDNO:1034, SEQ ID NO:1035, SEQ ID NO:1036, SEQ ID NO:1037, SEQ ID NO:1038,SEQ ID NO:1039, SEQ ID NO:1040, SEQ ID NO:1041, SEQ ID NO:1042, SEQ IDNO:1043, SEQ ID NO:1044, SEQ ID NO:1045, SEQ ID NO:1046, SEQ ID NO:1047,SEQ ID NO:1048, SEQ ID NO:1049, SEQ ID NO:1050, SEQ ID NO:1051, SEQ IDNO:1052, SEQ ID NO:1053, SEQ ID NO:1054, SEQ ID NO:1055, SEQ ID NO:1056,SEQ ID NO:1057, SEQ ID NO:1058, SEQ ID NO:1059, SEQ ID NO:1060, SEQ IDNO:1061, SEQ ID NO:1062, SEQ ID NO:1063, SEQ ID NO:1064, SEQ ID NO:1065,SEQ ID NO:1066, SEQ ID NO:1067, SEQ ID NO:1068, SEQ ID NO:1069, SEQ IDNO:1070, SEQ ID NO:1071, SEQ ID NO:1072, SEQ ID NO:1073, SEQ ID NO:1074,SEQ ID NO:1075, SEQ ID NO:1076, SEQ ID NO:1077, SEQ ID NO:1078, SEQ IDNO:1079, SEQ ID NO:1080, SEQ ID NO:1081, SEQ ID NO:1082, SEQ ID NO:1083,SEQ ID NO:1084, SEQ ID NO:1085, SEQ ID NO:1086, SEQ ID NO:1087, SEQ IDNO:1088, SEQ ID NO:1089, SEQ ID NO:1090, SEQ ID NO:1091, SEQ ID NO:1092,SEQ ID NO:1093, SEQ ID NO:1094, SEQ ID NO:1095, SEQ ID NO:1096, SEQ IDNO:1097, SEQ ID NO:1098, SEQ ID NO:1099, SEQ ID NO:1100, SEQ ID NO:1101,SEQ ID NO:1102, SEQ ID NO:1103, SEQ ID NO:1104, SEQ ID NO:1105, SEQ IDNO:1106, SEQ ID NO:1107, SEQ ID NO:1108, SEQ ID NO:1109, SEQ ID NO:1110,SEQ ID NO:1111, SEQ ID NO:1112, SEQ ID NO:1113, SEQ ID NO:1114, SEQ IDNO:1115, SEQ ID NO:1116, SEQ ID NO:1117, SEQ ID NO:1118, SEQ ID NO:1119,SEQ ID NO:1120, SEQ ID NO:1121, SEQ ID NO:1122, SEQ ID NO:1123, SEQ IDNO:1124, SEQ ID NO:1125, SEQ ID NO:1126, SEQ ID NO:1127, SEQ ID NO:1128,SEQ ID NO:1129, SEQ ID NO:1130, SEQ ID NO:1131, SEQ ID NO:1132, SEQ IDNO:1133, SEQ ID NO:1134, SEQ ID NO:1135, SEQ ID NO:1136, SEQ ID NO:1137,SEQ ID NO:1138, SEQ ID NO:1139, SEQ ID NO:1140, SEQ ID NO:1141, SEQ IDNO:1142, SEQ ID NO:1143, SEQ ID NO:1144, SEQ ID NO:1145, SEQ ID NO:1146,SEQ ID NO:1147, SEQ ID NO:1148, SEQ ID NO:1149, SEQ ID NO:1150, SEQ IDNO:1151, SEQ ID NO:1152, SEQ ID NO:1153, SEQ ID NO:1154, SEQ ID NO:1155,SEQ ID NO:1156, SEQ ID NO:1157, SEQ ID NO:1158, SEQ ID NO:1159, SEQ IDNO:1160, SEQ ID NO:1161, SEQ ID NO:1162, SEQ ID NO:1163, SEQ ID NO:1164,SEQ ID NO:1165, SEQ ID NO:1166, SEQ ID NO:1167, SEQ ID NO:1168, SEQ IDNO:1169, SEQ ID NO:1170, SEQ ID NO:1171, SEQ ID NO:1172, SEQ ID NO:1173,SEQ ID NO:1174, SEQ ID NO:1175, SEQ ID NO:1176, SEQ ID NO:1177, SEQ IDNO:1178, SEQ ID NO:1179, SEQ ID NO:1180, SEQ ID NO:1181, SEQ ID NO:1182,SEQ ID NO:1183, SEQ ID NO:1184, SEQ ID NO:1185, SEQ ID NO:1186, SEQ IDNO:1187, SEQ ID NO:1188, SEQ ID NO:1189, SEQ ID NO:1190, SEQ ID NO:1191,SEQ ID NO:1192, SEQ ID NO:1193, SEQ ID NO:1194, SEQ ID NO:1195, SEQ IDNO:1196, SEQ ID NO:1197, SEQ ID NO:1198, SEQ ID NO:1199, SEQ ID NO:1200,SEQ ID NO:1201, SEQ ID NO:1202, SEQ ID NO:1203, SEQ ID NO:1204, SEQ IDNO:1205, SEQ ID NO:1206, SEQ ID NO:1207, SEQ ID NO:1208, SEQ ID NO:1209,SEQ ID NO:1210, SEQ ID NO:1211, SEQ ID NO:1212, SEQ ID NO:1213, SEQ IDNO:1214, SEQ ID NO:1215, SEQ ID NO:1216, SEQ ID NO:1217, SEQ ID NO:1218,SEQ ID NO:1219, SEQ ID NO:1220, SEQ ID NO:1221, SEQ ID NO:1222, SEQ IDNO:1223, SEQ ID NO:1224, SEQ ID NO:1225, SEQ ID NO:1226, SEQ ID NO:1227,SEQ ID NO:1228, SEQ ID NO:1229, SEQ ID NO:1230, SEQ ID NO:1231, SEQ IDNO:1232, SEQ ID NO:1233, SEQ ID NO:1234, SEQ ID NO:1235, SEQ ID NO:1236,SEQ ID NO:1237, SEQ ID NO:1238, SEQ ID NO:1239, SEQ ID NO:1240, SEQ IDNO:1241, SEQ ID NO:1242, SEQ ID NO:1243, SEQ ID NO:1244, SEQ ID NO:1245,SEQ ID NO:1246, SEQ ID NO:1247, SEQ ID NO:1248, SEQ ID NO:1249, SEQ IDNO:1250, SEQ ID NO:1251, SEQ ID NO:1252, SEQ ID NO:1253, SEQ ID NO:1254,SEQ ID NO:1255, SEQ ID NO:1256, SEQ ID NO:1257, SEQ ID NO:1258, SEQ IDNO:1259, SEQ ID NO:1260, SEQ ID NO:1261, SEQ ID NO:1262, SEQ ID NO:1263,SEQ ID NO:1264, SEQ ID NO:1265, SEQ ID NO:1266, SEQ ID NO:1267, SEQ IDNO:1268, SEQ ID NO:1269, SEQ ID NO:1270, SEQ ID NO:1271, SEQ ID NO:1272,SEQ ID NO:1273, SEQ ID NO:1274, SEQ ID NO:1275, SEQ ID NO:1276, SEQ IDNO:1277, SEQ ID NO:1278, SEQ ID NO:1279, SEQ ID NO:1280, SEQ ID NO:1281,SEQ ID NO:1282, SEQ ID NO:1283, SEQ ID NO:1284, SEQ ID NO:1285, SEQ IDNO:1286, SEQ ID NO:1287, SEQ ID NO:1288, SEQ ID NO:1289, SEQ ID NO:1290,SEQ ID NO:1291, SEQ ID NO:1292, SEQ ID NO:1293, SEQ ID NO:1294, SEQ IDNO:1295, SEQ ID NO:1296, SEQ ID NO:1297, SEQ ID NO:1298, SEQ ID NO:1299,SEQ ID NO:1300, SEQ ID NO:1301, SEQ ID NO:1302, SEQ ID NO:1303, SEQ IDNO:1304, SEQ ID NO:1305, SEQ ID NO:1306, SEQ ID NO:1307, SEQ ID NO:1308,SEQ ID NO:1309, SEQ ID NO:1310, SEQ ID NO:1311, SEQ ID NO:1312, SEQ IDNO:1313, SEQ ID NO:1314, SEQ ID NO:1315, SEQ ID NO:1316, SEQ ID NO:1317,SEQ ID NO:1318, SEQ ID NO:1319, SEQ ID NO:1320, SEQ ID NO:1321, SEQ IDNO:1322, SEQ ID NO:1323, SEQ ID NO:1324, SEQ ID NO:1325, SEQ ID NO:1326,SEQ ID NO:1656, SEQ ID NO:1657, SEQ ID NO:1658, SEQ ID NO:1659, SEQ IDNO:1660, SEQ ID NO:1661, SEQ ID NO:1662, SEQ ID NO:1663, SEQ ID NO:1664,SEQ ID NO:1665, SEQ ID NO:1666, SEQ ID NO:1667, SEQ ID NO:1668, SEQ IDNO:1669, SEQ ID NO:1670, SEQ ID NO:1671, SEQ ID NO:1672, SEQ ID NO:1673,SEQ ID NO:1674, SEQ ID NO:1675, SEQ ID NO:1676, SEQ ID NO:1677, SEQ IDNO:1678, SEQ ID NO:1679, SEQ ID NO:1680, SEQ ID NO:1681, SEQ ID NO:1682,SEQ ID NO:1683, SEQ ID NO:1684, SEQ ID NO:1685, SEQ ID NO:1686, SEQ IDNO:1687, SEQ ID NO:1688, SEQ ID NO:1689, SEQ ID NO:1690, SEQ ID NO:1691,SEQ ID NO:1692, SEQ ID NO:1693, SEQ ID NO:1694, SEQ ID NO:1695, SEQ IDNO:1696, SEQ ID NO:1697, SEQ ID NO:1698, SEQ ID NO:1699, SEQ ID NO:1700,SEQ ID NO:1701, SEQ ID NO:1702, SEQ ID NO:1703, SEQ ID NO:1704, SEQ IDNO:1705, SEQ ID NO:1706, SEQ ID NO:1707, SEQ ID NO:1708, SEQ ID NO:1709,SEQ ID NO:1710, SEQ ID NO:1711, SEQ ID NO:1712, SEQ ID NO:1713, SEQ IDNO:1714, SEQ ID NO:1715, SEQ ID NO:1716, SEQ ID NO:1717, SEQ ID NO:1718,SEQ ID NO:1719, SEQ ID NO:1720, SEQ ID NO:1721, SEQ ID NO:1722, SEQ IDNO:1723, SEQ ID NO:1724, SEQ ID NO:1725, SEQ ID NO:1726, SEQ ID NO:1727,SEQ ID NO:1728, SEQ ID NO:1729, SEQ ID NO:1730, SEQ ID NO:1731, SEQ IDNO:1732, SEQ ID NO:1733, SEQ ID NO:1734, SEQ ID NO:1735, SEQ ID NO:1736,SEQ ID NO:1737, SEQ ID NO:1738, SEQ ID NO:1739, SEQ ID NO:1740, SEQ IDNO:1741, SEQ ID NO:1742, SEQ ID NO:1743, SEQ ID NO:1744, SEQ ID NO:1745,SEQ ID NO:1746, SEQ ID NO:1747, SEQ ID NO:1748, SEQ ID NO:1749, SEQ IDNO:1750, SEQ ID NO:1751, SEQ ID NO:1752, SEQ ID NO:1753, SEQ ID NO:1754,SEQ ID NO:1755, SEQ ID NO:1756, SEQ ID NO:1757, SEQ ID NO:1758, SEQ IDNO:1759, SEQ ID NO:1760, SEQ ID NO:1761, SEQ ID NO:1762, SEQ ID NO:1763,SEQ ID NO:1764, SEQ ID NO:1765, SEQ ID NO:1766, SEQ ID NO:1767, SEQ IDNO:1768, SEQ ID NO:1769, SEQ ID NO:1770, SEQ ID NO:1771, SEQ ID NO:1772,SEQ ID NO:1773, SEQ ID NO:1774, SEQ ID NO:1775, SEQ ID NO:1776, SEQ IDNO:1777, SEQ ID NO:1778, SEQ ID NO:1779, SEQ ID NO:1780, SEQ ID NO:1781,SEQ ID NO:1782, SEQ ID NO:1783, SEQ ID NO:1784, SEQ ID NO:1785, SEQ IDNO:1786, SEQ ID NO:1787, SEQ ID NO:1788, SEQ ID NO:1789, SEQ ID NO:1790,SEQ ID NO:1791, SEQ ID NO:1792, SEQ ID NO:1793, SEQ ID NO:1794, SEQ IDNO:1795, SEQ ID NO:1796, SEQ ID NO:1797, SEQ ID NO:1798, SEQ ID NO:1799,SEQ ID NO:1800, SEQ ID NO:1801, SEQ ID NO:1802, SEQ ID NO:1803, SEQ IDNO:1804, SEQ ID NO:1805, SEQ ID NO:1806, SEQ ID NO:1807, SEQ ID NO:1808,SEQ ID NO:1809, SEQ ID NO:1810, SEQ ID NO:1811, SEQ ID NO:1812, SEQ IDNO:1813, SEQ ID NO:1814, SEQ ID NO:1815, SEQ ID NO:1816, SEQ ID NO:1817,SEQ ID NO:1818, SEQ ID NO:1819, SEQ ID NO:1820, SEQ ID NO:1821, SEQ IDNO:1822, SEQ ID NO:1823, SEQ ID NO:1824, SEQ ID NO:1825, SEQ ID NO:1826,SEQ ID NO:1827, SEQ ID NO:1828, SEQ ID NO:1829, SEQ ID NO:1830, SEQ IDNO:1831, SEQ ID NO:1832, SEQ ID NO:1833, SEQ ID NO:1834, SEQ ID NO:1835,SEQ ID NO:1836, SEQ ID NO:1837, SEQ ID NO:1838, SEQ ID NO:1839, SEQ IDNO:1840, SEQ ID NO:1841, SEQ ID NO:1842, SEQ ID NO:1843, SEQ ID NO:1844,SEQ ID NO:1845, SEQ ID NO:1846, SEQ ID NO:1847, SEQ ID NO:1848, SEQ IDNO:1849, SEQ ID NO:1850, SEQ ID NO:1851, SEQ ID NO:1852, SEQ ID NO:1853,SEQ ID NO:1854, SEQ ID NO:1855, SEQ ID NO:1856, SEQ ID NO:1857, SEQ IDNO:1858, SEQ ID NO:1859, SEQ ID NO:1860, SEQ ID NO:1861, SEQ ID NO:1862,SEQ ID NO:1863, SEQ ID NO:1864, SEQ ID NO:1865, SEQ ID NO:1866, SEQ IDNO:1867, SEQ ID NO:1868, SEQ ID NO:1869, SEQ ID NO:1870, SEQ ID NO:1871,SEQ ID NO:1872, SEQ ID NO:1873, SEQ ID NO:1874, SEQ ID NO:1875, SEQ IDNO:1876, SEQ ID NO:1877, SEQ ID NO:1878, SEQ ID NO:1879, SEQ ID NO:1880,SEQ ID NO:1881, SEQ ID NO:1882, SEQ ID NO:1883, SEQ ID NO:1884, SEQ IDNO:1885, SEQ ID NO:1886, SEQ ID NO:1887, SEQ ID NO:1888, SEQ ID NO:1889,SEQ ID NO:1890, SEQ ID NO:1891, SEQ ID NO:1892, SEQ ID NO:1893, SEQ IDNO:1894, SEQ ID NO:1895, SEQ ID NO:1896, SEQ ID NO:1897, SEQ ID NO:1898,SEQ ID NO:1899, SEQ ID NO:1900, SEQ ID NO:1901, SEQ ID NO:1902, SEQ IDNO:1903, SEQ ID NO:1904, SEQ ID NO:1905, SEQ ID NO:1906, SEQ ID NO:1907,SEQ ID NO:1908, SEQ ID NO:1909, SEQ ID NO:1910, SEQ ID NO:1911, SEQ IDNO:1912, SEQ ID NO:1913, SEQ ID NO:1914, SEQ ID NO:1915, SEQ ID NO:1916,SEQ ID NO:1917, SEQ ID NO:1918, SEQ ID NO:1919, SEQ ID NO:1920, SEQ IDNO:1921, SEQ ID NO:1922, SEQ ID NO:1923, SEQ ID NO:1924, SEQ ID NO:1925,SEQ ID NO:1926, SEQ ID NO:1927, SEQ ID NO:1928, SEQ ID NO:1929, SEQ IDNO:1930, SEQ ID NO:1931, SEQ ID NO:1932, SEQ ID NO:1933, SEQ ID NO:1934,SEQ ID NO:1935, SEQ ID NO:1936, SEQ ID NO:1937, SEQ ID NO:1938, SEQ IDNO:1939, SEQ ID NO:1940, SEQ ID NO:1941, SEQ ID NO:1942, SEQ ID NO:1943,SEQ ID NO:1944, SEQ ID NO:1945, SEQ ID NO:1946, SEQ ID NO:1947, SEQ IDNO:1948, SEQ ID NO:1949, SEQ ID NO:1950, SEQ ID NO:1951, SEQ ID NO:1952,SEQ ID NO:1953, SEQ ID NO:1954, SEQ ID NO:1955, SEQ ID NO:1956, SEQ IDNO:1957, SEQ ID NO:1958, SEQ ID NO:1959, SEQ ID NO:1960, SEQ ID NO:1961,SEQ ID NO:1962, SEQ ID NO:1963, SEQ ID NO:1964, SEQ ID NO:1965, SEQ IDNO:1966, SEQ ID NO:1967, SEQ ID NO:1968, SEQ ID NO:1969, SEQ ID NO:1970,SEQ ID NO:1971, SEQ ID NO:1972, SEQ ID NO:1973, SEQ ID NO:1974, SEQ IDNO:1975, SEQ ID NO:1976, SEQ ID NO:1977, SEQ ID NO:1978, SEQ ID NO:1979,SEQ ID NO:1980, SEQ ID NO:1981, SEQ ID NO:1982, SEQ ID NO:1983, SEQ IDNO:1984, SEQ ID NO:1985, SEQ ID NO:1986, SEQ ID NO:1987, SEQ ID NO:1988,SEQ ID NO:1989, SEQ ID NO:1990, SEQ ID NO:1991, SEQ ID NO:1992, SEQ IDNO:1993, SEQ ID NO:1994, SEQ ID NO:1995, SEQ ID NO:1996, SEQ ID NO:1997,SEQ ID NO:1998, SEQ ID NO:1999, SEQ ID NO:2000, SEQ ID NO:2001, SEQ IDNO:2002, SEQ ID NO:2003, SEQ ID NO:2004, SEQ ID NO:2005, SEQ ID NO:2006,SEQ ID NO:2007, SEQ ID NO:2008, SEQ ID NO:2009, SEQ ID NO:2010, SEQ IDNO:2011, SEQ ID NO:2012, SEQ ID NO:2013, SEQ ID NO:2014, SEQ ID NO:2015,SEQ ID NO:2016, SEQ ID NO:2017, SEQ ID NO:2018, SEQ ID NO:2019, SEQ IDNO:2020, SEQ ID NO:2021, SEQ ID NO:2022, SEQ ID NO:2023, SEQ ID NO:2024,SEQ ID NO:2025, SEQ ID NO:2026, SEQ ID NO:2027, SEQ ID NO:2028, SEQ IDNO:2029, SEQ ID NO:2030, SEQ ID NO:2031, SEQ ID NO:2032, SEQ ID NO:2033,SEQ ID NO:2034, SEQ ID NO:2035, SEQ ID NO:2036, SEQ ID NO:2037, SEQ IDNO:2038, SEQ ID NO:2039, SEQ ID NO:2040, SEQ ID NO:2041, SEQ ID NO:2042,SEQ ID NO:2043, SEQ ID NO:2044, SEQ ID NO:2045, SEQ ID NO:2046, SEQ IDNO:2047, SEQ ID NO:2048, SEQ ID NO:2049, SEQ ID NO:2050, SEQ ID NO:2051,SEQ ID NO:2052, SEQ ID NO:2053, SEQ ID NO:2054, SEQ ID NO:2055, SEQ IDNO:2056, SEQ ID NO:2057, SEQ ID NO:2058, SEQ ID NO:2059, SEQ ID NO:2060,SEQ ID NO:2061, SEQ ID NO:2062, SEQ ID NO:2063, SEQ ID NO:2064, SEQ IDNO:2065, SEQ ID NO:2066, SEQ ID NO:2067, SEQ ID NO:2068, SEQ ID NO:2069,SEQ ID NO:2070, SEQ ID NO:2071, SEQ ID NO:2072, SEQ ID NO:2073, SEQ IDNO:2074, SEQ ID NO:2075, SEQ ID NO:2076, SEQ ID NO:2077, SEQ ID NO:2078,SEQ ID NO:2079, SEQ ID NO:2080, SEQ ID NO:2081, SEQ ID NO:2082, SEQ IDNO:2083, SEQ ID NO:2084, SEQ ID NO:2085, SEQ ID NO:2086, SEQ ID NO:2087,SEQ ID NO:2088, SEQ ID NO:2089, SEQ ID NO:2090, SEQ ID NO:2091, SEQ IDNO:2092, SEQ ID NO:2093, SEQ ID NO:2094, SEQ ID NO:2095, SEQ ID NO:2096,SEQ ID NO:2097, SEQ ID NO:2098, SEQ ID NO:2099, SEQ ID NO:2100, SEQ IDNO:2101, SEQ ID NO:2102, SEQ ID NO:2103, SEQ ID NO:2104, SEQ ID NO:2105,SEQ ID NO:2106, SEQ ID NO:2107, SEQ ID NO:2108, SEQ ID NO:2109, SEQ IDNO:2110, SEQ ID NO:2111, SEQ ID NO:2112, SEQ ID NO:2113, SEQ ID NO:2114,SEQ ID NO:2115, SEQ ID NO:2116, SEQ ID NO:2117, SEQ ID NO:2118, SEQ IDNO:2119, SEQ ID NO:2120, SEQ ID NO:2121, SEQ ID NO:2122, SEQ ID NO:2123,SEQ ID NO:2124, SEQ ID NO:2125, SEQ ID NO:2126, SEQ ID NO:2127, SEQ IDNO:2128, SEQ ID NO:2129, SEQ ID NO:2130, SEQ ID NO:2131, SEQ ID NO:2132,SEQ ID NO:2133, SEQ ID NO:2134, SEQ ID NO:2135, SEQ ID NO:2136, SEQ IDNO:2137, SEQ ID NO:2138, SEQ ID NO:2139, SEQ ID NO:2140, SEQ ID NO:2141,SEQ ID NO:2142, SEQ ID NO:2143, SEQ ID NO:2144, SEQ ID NO:2145, SEQ IDNO:2146, SEQ ID NO:2147, SEQ ID NO:2148, SEQ ID NO:2149, SEQ ID NO:2150,SEQ ID NO:2151. Alternatively, the PCR uses corresponding probesconsisting of nucleotide sequences selected from the group consisting ofSEQ ID NO:1327, SEQ ID NO:1328, SEQ ID NO:1329, SEQ ID NO:1330, SEQ IDNO:1331, SEQ ID NO:1332, SEQ ID NO:1333, SEQ ID NO:1334, SEQ ID NO:1335,SEQ ID NO:1336, SEQ ID NO:1337, SEQ ID NO:1338, SEQ ID NO:1339, SEQ IDNO:1340, SEQ ID NO:1341, SEQ ID NO:1342, SEQ ID NO:1343, SEQ ID NO:1344,SEQ ID NO:1345, SEQ ID NO:1346, SEQ ID NO:1347, SEQ ID NO:1348, SEQ IDNO:1349, SEQ ID NO:1350, SEQ ID NO:1351, SEQ ID NO:1352, SEQ ID NO:1353,SEQ ID NO:1354, SEQ ID NO:1355, SEQ ID NO:1356, SEQ ID NO:1357, SEQ IDNO:1358, SEQ ID NO:1359, SEQ ID NO:1360, SEQ ID NO:1361, SEQ ID NO:1362,SEQ ID NO:1363, SEQ ID NO:1364, SEQ ID NO:1365, SEQ ID NO:1366, SEQ IDNO:1367, SEQ ID NO:1368, SEQ ID NO:1369, SEQ ID NO:1370, SEQ ID NO:1371,SEQ ID NO:1372, SEQ ID NO:1373, SEQ ID NO:1374, SEQ ID NO:1375, SEQ IDNO:1376, SEQ ID NO:1377, SEQ ID NO:1378, SEQ ID NO:1379, SEQ ID NO:1380,SEQ ID NO:1381, SEQ ID NO:1382, SEQ ID NO:1383, SEQ ID NO:1384, SEQ IDNO:1385, SEQ ID NO:1386, SEQ ID NO:1387, SEQ ID NO:1388, SEQ ID NO:1389,SEQ ID NO:1390, SEQ ID NO:1391, SEQ ID NO:1392, SEQ ID NO:1393, SEQ IDNO:1394, SEQ ID NO:1395, SEQ ID NO:1396, SEQ ID NO:1397, SEQ ID NO:1398,SEQ ID NO:1399, SEQ ID NO:1400, SEQ ID NO:1401, SEQ ID NO:1402, SEQ IDNO:1403, SEQ ID NO:1404, SEQ ID NO:1405, SEQ ID NO:1406, SEQ ID NO:1407,SEQ ID NO:1408, SEQ ID NO:1409, SEQ ID NO:1410, SEQ ID NO:1411, SEQ IDNO:1412, SEQ ID NO:1413, SEQ ID NO:1414, SEQ ID NO:1415, SEQ ID NO:1416,SEQ ID NO:1417, SEQ ID NO:1418, SEQ ID NO:1419, SEQ ID NO:1420, SEQ IDNO:1421, SEQ ID NO:1422, SEQ ID NO:1423, SEQ ID NO:1424, SEQ ID NO:1425,SEQ ID NO:1426, SEQ ID NO:1427, SEQ ID NO:1428, SEQ ID NO:1429, SEQ IDNO:1430, SEQ ID NO:1431, SEQ ID NO:1432, SEQ ID NO:1433, SEQ ID NO:1434,SEQ ID NO:1435, SEQ ID NO:1436, SEQ ID NO:1437, SEQ ID NO:1438, SEQ IDNO:1439, SEQ ID NO:1440, SEQ ID NO:1441, SEQ ID NO:1442, SEQ ID NO:1443,SEQ ID NO:1444, SEQ ID NO:1445, SEQ ID NO:1446, SEQ ID NO:1447, SEQ IDNO:1448, SEQ ID NO:1449, SEQ ID NO:1450, SEQ ID NO:1451, SEQ ID NO:1452,SEQ ID NO:1454, SEQ ID NO:1455, SEQ ID NO:1456, SEQ ID NO:1457, SEQ IDNO:1458, SEQ ID NO:1459, SEQ ID NO:1460, SEQ ID NO:1461, SEQ ID NO:1462,SEQ ID NO:1463, SEQ ID NO:1464, SEQ ID NO:1465, SEQ ID NO:1466, SEQ IDNO:1467, SEQ ID NO:1468, SEQ ID NO:1469, SEQ ID NO:1470, SEQ ID NO:1471,SEQ ID NO:1472, SEQ ID NO:1473, SEQ ID NO:1474, SEQ ID NO:1475, SEQ IDNO:1476, SEQ ID NO:1477, SEQ ID NO:1478, SEQ ID NO:1479, SEQ ID NO:1480,SEQ ID NO:1481, SEQ ID NO:1482, SEQ ID NO:1483, SEQ ID NO:1484, SEQ IDNO:1485, SEQ ID NO:1486, SEQ ID NO:1487, SEQ ID NO:1488, SEQ ID NO:1489,SEQ ID NO:1490, SEQ ID NO:1491, SEQ ID NO:1492, SEQ ID NO:1493, SEQ IDNO:1494, SEQ ID NO:1495, SEQ ID NO:1496, SEQ ID NO:1497, SEQ ID NO:1498,SEQ ID NO:1499, SEQ ID NO:1500, SEQ ID NO:1501, SEQ ID NO:1502, SEQ IDNO:1503, SEQ ID NO:1504, SEQ ID NO:1505, SEQ ID NO:1506, SEQ ID NO:1507,SEQ ID NO:1508, SEQ ID NO:1509, SEQ ID NO:1510, SEQ ID NO:1511, SEQ IDNO:1512, SEQ ID NO:1513, SEQ ID NO:1514, SEQ ID NO:1515, SEQ ID NO:1516,SEQ ID NO:1517, SEQ ID NO:1518, SEQ ID NO:1519, SEQ ID NO:1520, SEQ IDNO:1521, SEQ ID NO:1522, SEQ ID NO:1523, SEQ ID NO:1524, SEQ ID NO:1525,SEQ ID NO:1526, SEQ ID NO:1527, SEQ ID NO:1528, SEQ ID NO:1529, SEQ IDNO:1530, SEQ ID NO:1531, SEQ ID NO:1532, SEQ ID NO:1533, SEQ ID NO:1534,SEQ ID NO:1535, SEQ ID NO:1536, SEQ ID NO:1537, SEQ ID NO:1538, SEQ IDNO:1539, SEQ ID NO:1540, SEQ ID NO:1541, SEQ ID NO:1542, SEQ ID NO:1543,SEQ ID NO:1544, SEQ ID NO:1545, SEQ ID NO:1546, SEQ ID NO:1547, SEQ IDNO:1548, SEQ ID NO:1549, SEQ ID NO:1550, SEQ ID NO:1551, SEQ ID NO:1552,SEQ ID NO:1553, SEQ ID NO:1554, SEQ ID NO:1555, SEQ ID NO:1556, SEQ IDNO:1557, SEQ ID NO:1558, SEQ ID NO:1559, SEQ ID NO:1560, SEQ ID NO:1561,SEQ ID NO:1562, SEQ ID NO:1563, SEQ ID NO:1564, SEQ ID NO:1565, SEQ IDNO:1566, SEQ ID NO:1567, SEQ ID NO:1568, SEQ ID NO:1569, SEQ ID NO:1570,SEQ ID NO:1571, SEQ ID NO:1572, SEQ ID NO:1573, SEQ ID NO:1574, SEQ IDNO:1575, SEQ ID NO:1576, SEQ ID NO:1577, SEQ ID NO:1578, SEQ ID NO:1579,SEQ ID NO:1580, SEQ ID NO:1581, SEQ ID NO:1582, SEQ ID NO:1583, SEQ IDNO:1584, SEQ ID NO:1585, SEQ ID NO:1586, SEQ ID NO:1587, SEQ ID NO:1588,SEQ ID NO:1589, SEQ ID NO:1590, SEQ ID NO:1591, SEQ ID NO:1592, SEQ IDNO:1593, SEQ ID NO:1594, SEQ ID NO, SEQ ID NO:1595, SEQ ID NO:1596, SEQID NO:1597, SEQ ID NO:1598, SEQ ID NO:1599, SEQ ID NO:1600, SEQ IDNO:1601, SEQ ID NO:1602, SEQ ID NO:1603, SEQ ID NO:1604, SEQ ID NO:1605,SEQ ID NO:1606, SEQ ID NO:1607, SEQ ID NO:1608, SEQ ID NO:1609, SEQ IDNO:1610, SEQ ID NO:1611, SEQ ID NO:1612, SEQ ID NO:1613, SEQ ID NO:1614,SEQ ID NO:1615, SEQ ID NO:1616, SEQ ID NO:1617, SEQ ID NO:1618, SEQ IDNO:1619, SEQ ID NO:1620, SEQ ID NO:1621, SEQ ID NO:1622, SEQ ID NO:1623,SEQ ID NO:1624, SEQ ID NO:1625, SEQ ID NO:1626, SEQ ID NO:1627, SEQ IDNO:1628, SEQ ID NO:1629, SEQ ID NO:1630, SEQ ID NO:1631, SEQ ID NO:1632,SEQ ID NO:1633, SEQ ID NO:1634, SEQ ID NO:1635, SEQ ID NO:1636, SEQ IDNO:1637, SEQ ID NO:1638, SEQ ID NO:1639, SEQ ID NO:1640, SEQ ID NO:1641,SEQ ID NO:1642, SEQ ID NO:1643, SEQ ID NO:1644, SEQ ID NO:1645, SEQ IDNO:1646, SEQ ID NO:1647, SEQ ID NO:1648, SEQ ID NO:1649, SEQ ID NO:1650,SEQ ID NO:1651, SEQ ID NO:1652, SEQ ID NO:1653, SEQ ID NO:1654, SEQ IDNO:1655, SEQ ID NO:1656, SEQ ID NO:1657, SEQ ID NO:2152, SEQ ID NO, SEQID NO:2153, SEQ ID NO, SEQ ID NO:2154, SEQ ID NO, SEQ ID NO, SEQ ID NO,SEQ ID NO:2145, SEQ ID NO, SEQ ID NO:2156, SEQ ID NO:2157, SEQ IDNO:2158, SEQ ID NO:2159, SEQ ID NO, SEQ ID NO:2160, SEQ ID NO:2161, SEQID NO:2162, SEQ ID NO:2163, SEQ ID NO:2164, SEQ ID NO, SEQ ID NO:2165,SEQ ID NO, SEQ ID NO:2166, SEQ ID NO:2167, SEQ ID NO:2168, SEQ IDNO:2169, SEQ ID NO:2170, SEQ ID NO:2171, SEQ ID NO:2172, SEQ ID NO:2173,SEQ ID NO:2174, SEQ ID NO:2175, SEQ ID NO:2176, SEQ ID NO:2177, SEQ IDNO:2178, SEQ ID NO:2179, SEQ ID NO:2180, SEQ ID NO:2181, SEQ ID NO:2182,SEQ ID NO:2183, SEQ ID NO:2184, SEQ ID NO:2185, SEQ ID NO:2186, SEQ IDNO:2187, SEQ ID NO:2188, SEQ ID NO:2189, SEQ ID NO:2190, SEQ ID NO:2191,SEQ ID NO:2192, SEQ ID NO:2193, SEQ ID NO:2194, SEQ ID NO:2195, SEQ IDNO:2196, SEQ ID NO:2197, SEQ ID NO:2198, SEQ ID NO:2199, SEQ ID NO:2200,SEQ ID NO:2219, SEQ ID NO:2202, SEQ ID NO:2203, SEQ ID NO:2204, SEQ IDNO:2205, SEQ ID NO:2206, SEQ ID NO:2207, SEQ ID NO:2208, SEQ ID NO:2209,SEQ ID NO:2210, SEQ ID NO:2211, SEQ ID NO:2212, SEQ ID NO:2213, SEQ IDNO:2214, SEQ ID NO:2215, SEQ ID NO:2216, SEQ ID NO:2217, SEQ ID NO:2218,SEQ ID NO:2219, SEQ ID NO:2220, SEQ ID NO:2221, SEQ ID NO:2222, SEQ IDNO:2223, SEQ ID NO:2224, SEQ ID NO:2225, SEQ ID NO:2226, SEQ ID NO:2227,SEQ ID NO:2228, SEQ ID NO:2229, SEQ ID NO:2230, SEQ ID NO:2231, SEQ IDNO:2232, SEQ ID NO:2233, SEQ ID NO:2234, SEQ ID NO:2235, SEQ ID NO:2236,SEQ ID NO:2237, SEQ ID NO:2238, SEQ ID NO:2239, SEQ ID NO:2240, SEQ IDNO:2241, SEQ ID NO:2242, SEQ ID NO:2243, SEQ ID NO:2244, SEQ ID NO:2245,SEQ ID NO:2246, SEQ ID NO:2247, SEQ ID NO:2248, SEQ ID NO:2249, SEQ IDNO:2250, SEQ ID NO:2251, SEQ ID NO:2252, SEQ ID NO:2253, SEQ ID NO:2254,SEQ ID NO:2255, SEQ ID NO:2256, SEQ ID NO:2257, SEQ ID NO:2258, SEQ IDNO:2259, SEQ ID NO:2260, SEQ ID NO:2261, SEQ ID NO:2262, SEQ ID NO:2263,SEQ ID NO:2264, SEQ ID NO:2265, SEQ ID NO:2266, SEQ ID NO:2267, SEQ IDNO:2268, SEQ ID NO:2269, SEQ ID NO:2270, SEQ ID NO:2271, SEQ ID NO:2272,SEQ ID NO:2273, SEQ ID NO:2274, SEQ ID NO:2275, SEQ ID NO:2276, SEQ IDNO:2277, SEQ ID NO:2278, SEQ ID NO:2279, SEQ ID NO:2280, SEQ ID NO:2281,SEQ ID NO:2282, SEQ ID NO:2283, SEQ ID NO:2284, SEQ ID NO:2285, SEQ IDNO:2286, SEQ ID NO:2287, SEQ ID NO:2288, SEQ ID NO:2289, SEQ ID NO:2290,SEQ ID NO:2291, SEQ ID NO:2292, SEQ ID NO:2293, SEQ ID NO:2294, SEQ IDNO:2295, SEQ ID NO:2296, SEQ ID NO:2297, SEQ ID NO:2298, SEQ ID NO:2299,SEQ ID NO:2300, SEQ ID NO:2301, SEQ ID NO:2302, SEQ ID NO:2303, SEQ IDNO:2304, SEQ ID NO:2305, SEQ ID NO:2306, SEQ ID NO:2307, SEQ ID NO:2308,SEQ ID NO:2309, SEQ ID NO:2310, SEQ ID NO:2310, SEQ ID NO:2312, SEQ IDNO:2303, SEQ ID NO:2314, SEQ ID NO:2315, SEQ ID NO:2316, SEQ ID NO:2317,SEQ ID NO:2318, SEQ ID NO:2319, SEQ ID NO:2320, SEQ ID NO:2321, SEQ IDNO:2322, SEQ ID NO:2323, SEQ ID NO:2324, SEQ ID NO:2325, SEQ ID NO:2326,SEQ ID NO:2327, SEQ ID NO:2328, SEQ ID NO:2329, SEQ ID NO:2330, SEQ IDNO:2331, SEQ ID NO:2332, SEQ ID NO:2333, SEQ ID NO:2334, SEQ ID NO:2335,SEQ ID NO:2336, SEQ ID NO:2337, SEQ ID NO:2338, SEQ ID NO:2339, SEQ IDNO:2340, SEQ ID NO:2341, SEQ ID NO:2342, SEQ ID NO:2343, SEQ ID NO:2344,SEQ ID NO:2345, SEQ ID NO:2346, SEQ ID NO:2347, SEQ ID NO:2348, SEQ IDNO:2349, SEQ ID NO:2350, SEQ ID NO:2351, SEQ ID NO:2352, SEQ ID NO:2353,SEQ ID NO:2354, SEQ ID NO:2355, SEQ ID NO:2356, SEQ ID NO:2357, SEQ IDNO:2358, SEQ ID NO:2359, SEQ ID NO:2360, SEQ ID NO:2361, SEQ ID NO:2362,SEQ ID NO:2363, SEQ ID NO:2364, SEQ ID NO:2365, SEQ ID NO:2366, SEQ IDNO:2367, SEQ ID NO:2368, SEQ ID NO:2369, SEQ ID NO:2370, SEQ ID NO:2371,SEQ ID NO:2372, SEQ ID NO:2373, SEQ ID NO:2374, SEQ ID NO:2375, SEQ IDNO:2376, SEQ ID NO:2377, SEQ ID NO:2378, SEQ ID NO:2379, SEQ ID NO:2380,SEQ ID NO:2381, SEQ ID NO:2382, SEQ ID NO:2383, SEQ ID NO:2384, SEQ IDNO:2385, SEQ ID NO:2386, SEQ ID NO:2387, SEQ ID NO:2388, SEQ ID NO:2389,SEQ ID NO:2390, SEQ ID NO:2391, SEQ ID NO:2392, SEQ ID NO:2393, SEQ IDNO:2394, SEQ ID NO:2395, SEQ ID NO:2396, SEQ ID NO:2397, SEQ ID NO:2398,SEQ ID NO:2399. The RNA level may be detected by hybridization to theprobes. In a further variation, the RNA level is detected byhybridization to an oligonucleotide. Examples of oligonucleotide includeoligonucleotides having a nucleotide sequence selected from SEQ ID NO:2,SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ IDNO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ IDNO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ IDNO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ IDNO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ IDNO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ IDNO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ IDNO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ IDNO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ IDNO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ IDNO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ IDNO:58, SEQ ID NO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ IDNO:63, SEQ ID NO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, SEQ IDNO:68, SEQ ID NO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ ID NO:72, SEQ IDNO:73, SEQ ID NO:74, SEQ ID NO:75, SEQ ID NO:76, SEQ ID NO:77, SEQ IDNO:78, SEQ ID NO:79, SEQ ID NO:80, SEQ ID NO:81, SEQ ID NO:82, SEQ IDNO:83, SEQ ID NO:84, SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87, SEQ IDNO:88, SEQ ID NO:89, SEQ ID NO:90, SEQ ID NO:91, SEQ ID NO:92, SEQ IDNO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ IDNO:98, SEQ ID NO:99, SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ IDNO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, SEQID NO:108, SEQ ID NO:109, SEQ ID NO:110, SEQ ID NO:111, SEQ ID NO:112,SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ IDNO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQID NO:122, SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126,SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ IDNO:131, SEQ ID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQ ID NO:135, SEQID NO:136, SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139, SEQ ID NO:140,SEQ ID NO:141, SEQ ID NO:142, SEQ ID NO:143, SEQ ID NO:144, SEQ IDNO:145, SEQ ID NO:146, SEQ ID NO:147, SEQ ID NO:148, SEQ ID NO:149, SEQID NO:150, SEQ ID NO:151, SEQ ID NO:152, SEQ ID NO:153, SEQ ID NO:154,SEQ ID NO:155, SEQ ID NO:156, SEQ ID NO:157, SEQ ID NO:158, SEQ IDNO:159, SEQ ID NO:160, SEQ ID NO:161, SEQ ID NO:162, SEQ ID NO:163, SEQID NO:164, SEQ ID NO:165, SEQ ID NO:166, SEQ ID NO:167, SEQ ID NO:168,SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171, SEQ ID NO:172, SEQ IDNO:173, SEQ ID NO:174, SEQ ID NO:175, SEQ ID NO:176, SEQ ID NO:177, SEQID NO:178, SEQ ID NO:179, SEQ ID NO:180, SEQ ID NO:181, SEQ ID NO:182,SEQ ID NO:183, SEQ ID NO:184, SEQ ID NO:185, SEQ ID NO:186, SEQ IDNO:187, SEQ ID NO:188, SEQ ID NO:189, SEQ ID NO:190, SEQ ID NO:191, SEQID NO:192, SEQ ID NO:193, SEQ ID NO:194, SEQ ID NO:195, SEQ ID NO:196,SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199, SEQ ID NO:200, SEQ IDNO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ ID NO:204, SEQ ID NO:205, SEQID NO:206, SEQ ID NO:207, SEQ ID NO:208, SEQ ID NO:209, SEQ ID NO:210,SEQ ID NO:211, SEQ ID NO:212, SEQ ID NO:213, SEQ ID NO:214, SEQ IDNO:215, SEQ ID NO:216, SEQ ID NO:217, SEQ ID NO:218, SEQ ID NO:219, SEQID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQ ID NO:223, SEQ ID NO:224,SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227, SEQ ID NO:228, SEQ IDNO:229, SEQ ID NO:230, SEQ ID NO:231, SEQ ID NO:232, SEQ ID NO:233, SEQID NO:234, SEQ ID NO:235, SEQ ID NO:236, SEQ ID NO:237, SEQ ID NO:238,SEQ ID NO:239, SEQ ID NO:240, SEQ ID NO:241, SEQ ID NO:242, SEQ IDNO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ ID NO:246, SEQ ID NO:247, SEQID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQ ID NO:251, SEQ ID NO:252,SEQ ID NO:253, SEQ ID NO:254, SEQ ID NO:255, SEQ ID NO:256, SEQ IDNO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ ID NO:260, SEQ ID NO:261, SEQID NO:262, SEQ ID NO:263, SEQ ID NO:264, SEQ ID NO:265, SEQ ID NO:266,SEQ ID NO:267, SEQ ID NO:268, SEQ ID NO:269, SEQ ID NO:270, SEQ IDNO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ ID NO:274, SEQ ID NO:275, SEQID NO:276, SEQ ID NO:277, SEQ ID NO:278, SEQ ID NO:279, SEQ ID NO:280,SEQ ID NO:281, SEQ ID NO:282, SEQ ID NO:283, SEQ ID NO:284, SEQ IDNO:285, SEQ ID NO:286, SEQ ID NO:287, SEQ ID NO:288, SEQ ID NO:289, SEQID NO:290, SEQ ID NO:291, SEQ ID NO:292, SEQ ID NO:293, SEQ ID NO:294,SEQ ID NO:295, SEQ ID NO:296, SEQ ID NO:297, SEQ ID NO:298, SEQ IDNO:299, SEQ ID NO:300, SEQ ID NO:301, SEQ ID NO:302, SEQ ID NO:303, SEQID NO:304, SEQ ID NO:305, SEQ ID NO:306, SEQ ID NO:307, SEQ ID NO:308,SEQ ID NO:309, SEQ ID NO:310, SEQ ID NO:311, SEQ ID NO:312, SEQ IDNO:313, SEQ ID NO:314, SEQ ID NO:315, SEQ ID NO:316, SEQ ID NO:317, SEQID NO:318, SEQ ID NO:319, SEQ ID NO:320, SEQ ID NO:321, SEQ ID NO:322,SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325, SEQ ID NO:326, SEQ IDNO:327, SEQ ID NO:328, SEQ ID NO:329, SEQ ID NO:330, SEQ ID NO:331, SEQID NO:332, SEQ ID NO:2697, SEQ ID NO:2645, SEQ ID NO:2707, SEQ IDNO:2679, SEQ ID NO:2717, SEQ ID NO:2646, SEQ ID NO:2667, SEQ ID NO:2706,SEQ ID NO:2740, SEQ ID NO:2669, SEQ ID NO:2674, SEQ ID NO:2743, SEQ IDNO:2716, SEQ ID NO:2727, SEQ ID NO:2721, SEQ ID NO:2641, SEQ ID NO:2671,SEQ ID NO:2752, SEQ ID NO:2737, SEQ ID NO:2719, SEQ ID NO:2684, SEQ IDNO:2677, SEQ ID NO:2748, SEQ ID NO:2703, SEQ ID NO:2711, SEQ ID NO:2663,SEQ ID NO:2657, SEQ ID NO:2683, SEQ ID NO:2686, SEQ ID NO:2687, SEQ IDNO:2644, SEQ ID NO:2664, SEQ ID NO:2747, SEQ ID NO:2744, SEQ ID NO:2678,SEQ ID NO:2731, SEQ ID NO:2713, SEQ ID NO:2736, SEQ ID NO:2708, SEQ IDNO:2670, SEQ ID NO:2661, SEQ ID NO:2680, SEQ ID NO:2754, SEQ ID NO:2728,SEQ ID NO:2742, SEQ ID NO:2668, SEQ ID NO:2750, SEQ ID NO:2746, SEQ IDNO:2738, SEQ ID NO:2627, SEQ ID NO:2739, SEQ ID NO:2647, SEQ ID NO:2628,SEQ ID NO:2638, SEQ ID NO:2725, SEQ ID NO:2714, SEQ ID NO:2635, SEQ IDNO:2751, SEQ ID NO:2629, SEQ ID NO:2695, SEQ ID NO:2741, SEQ ID NO:2691,SEQ ID NO:2726, SEQ ID NO:2722, SEQ ID NO:2689, SEQ ID NO:2734, SEQ IDNO:2631, SEQ ID NO:2656, SEQ ID NO:2696, SEQ ID NO:2676, SEQ ID NO:2701,SEQ ID NO:2730, SEQ ID NO:2710, SEQ ID NO:2632, SEQ ID NO:2724, SEQ IDNO:2698, SEQ ID NO:2662, SEQ ID NO:2753, SEQ ID NO:2704, SEQ ID NO:2675,SEQ ID NO:2700, SEQ ID NO:2640, SEQ ID NO:2723, SEQ ID NO:2658, SEQ IDNO:2688, SEQ ID NO:2735, SEQ ID NO:2702, SEQ ID NO:2681, SEQ ID NO:2755,SEQ ID NO:2715, SEQ ID NO:2732, SEQ ID NO:2652, SEQ ID NO:2651, SEQ IDNO:2718, SEQ ID NO:2673, SEQ ID NO:2733, SEQ ID NO:2712, SEQ ID NO:2659,SEQ ID NO:2654, SEQ ID NO:2636, SEQ ID NO:2639, SEQ ID NO:2690, SEQ IDNO:2705, SEQ ID NO:2685, SEQ ID NO:2692, SEQ ID NO:2693, SEQ ID NO:2648,SEQ ID NO:2650, SEQ ID NO:2720, SEQ ID NO:2660, SEQ ID NO:2666, SEQ IDNO:2699, SEQ ID NO:2633, SEQ ID NO:2672, SEQ ID NO:2642, SEQ ID NO:2682,SEQ ID NO:2655, SEQ ID NO:2630, SEQ ID NO:2745, SEQ ID NO:2643, SEQ IDNO:2694, SEQ ID NO:2749, SEQ ID NO:2665, SEQ ID NO:2649, SEQ ID NO:2637,SEQ ID NO:2634, SEQ ID NO:2709, SEQ ID NO:2653, SEQ ID NO:2729. In afurther variation, the oligonucleotide has the nucleotide sequence SEQID NO:36. In still a further variation, the oligonucleotide has thenucleotide sequence SEQ ID NO:87. In yet a further variation, theoligonucleotide has the nucleotide sequence SEQ ID NO:94. In anadditional variation, the oligonucleotide has a nucleotide sequenceconsisting of SEQ ID NO:91. In another variation, the oligonucleotidehas a nucleotide sequence consisting of SEQ ID NO: 107. Theoligonucleotide may be DNA, RNA, cDNA, PNA, genomic DNA, or syntheticoligonucleotides.

In another aspect, the methods of detecting transplant rejection includedetecting the expression level by measuring one or more proteinsexpressed by the one or more genes. In one variation, the one or moreproteins include an amino acid sequence selected from SEQ ID NO:2400,SEQ ID NO:2401, SEQ ID NO:2402, SEQ ID NO:2403, SEQ ID NO:2404, SEQ IDNO:2405, SEQ ID NO:2407, SEQ ID NO:2408, SEQ ID NO:2409, SEQ ID NO:2410,SEQ ID NO:2411, SEQ ID NO:2412, SEQ ID NO:2413, SEQ ID NO:2414, SEQ IDNO:2415, SEQ ID NO:2416, SEQ ID NO:2417, SEQ ID NO:2418, SEQ ID NO:2419,SEQ ID NO:2420, SEQ ID NO:2421, SEQ ID NO:2422, SEQ ID NO:2423, SEQ IDNO:2424, SEQ ID NO:2425, SEQ ID NO:2426, SEQ ID NO:2427, SEQ ID NO:2428,SEQ ID NO:2429, SEQ ID NO:2430, SEQ ID NO:2432, SEQ ID NO:2433, SEQ IDNO:2434, SEQ ID NO:2435, SEQ ID NO:2436, SEQ ID NO:2437, SEQ ID NO:2438,SEQ ID NO:2439, SEQ ID NO:2440, SEQ ID NO:2441, SEQ ID NO:2442, SEQ IDNO:2443, SEQ ID NO:2444, SEQ ID NO:2445, SEQ ID NO:2446, SEQ ID NO:2447,SEQ ID NO:2448, SEQ ID NO:2449, SEQ ID NO:2450, SEQ ID NO:2451, SEQ IDNO:2452, SEQ ID NO:2453, SEQ ID NO:2454, SEQ ID NO:2455, SEQ ID NO:2456,SEQ ID NO:2457, SEQ ID NO:2458, SEQ ID NO:2459, SEQ ID NO:2460, SEQ IDNO:2461, SEQ ID NO:2462, SEQ ID NO:2463, SEQ ID NO:2464, SEQ ID NO:2465,SEQ ID NO:2466, SEQ ID NO:2467, SEQ ID NO:2468, SEQ ID NO:2469, SEQ IDNO:2470, SEQ ID NO:2478, SEQ ID NO:2479, SEQ ID NO:2480, SEQ ID NO:2481,SEQ ID NO:2482, SEQ ID NO:2483, SEQ ID NO:2485, SEQ ID NO:2486, SEQ IDNO:2488, SEQ ID NO:2491, SEQ ID NO:2492, SEQ ID NO:2493, SEQ ID NO:2494,SEQ ID NO:2495, SEQ ID NO:2496, SEQ ID NO:2497, SEQ ID NO:2502, SEQ IDNO:2503, SEQ ID NO:2504, SEQ ID NO:2505, SEQ ID NO:2506, SEQ ID NO:2507,SEQ ID NO:2508, SEQ ID NO:2509, SEQ ID NO:2510, SEQ ID NO:2511, SEQ IDNO:2512, SEQ ID NO:2513, SEQ ID NO:2514, SEQ ID NO:2515, SEQ ID NO:2516,SEQ ID NO:2517, SEQ ID NO:2518, SEQ ID NO:2519, SEQ ID NO:2520, SEQ IDNO:2521, SEQ ID NO:2528, SEQ ID NO:2529, SEQ ID NO:2530, SEQ ID NO:2531,SEQ ID NO:2532, SEQ ID NO:2533, SEQ ID NO:2534, SEQ ID NO:2535, SEQ IDNO:2536, SEQ ID NO:2537, SEQ ID NO:2538, SEQ ID NO:2539, SEQ ID NO:2540,SEQ ID NO:2541, SEQ ID NO:2542, SEQ ID NO:2543, SEQ ID NO:2544, SEQ IDNO:2545, SEQ ID NO:2546, SEQ ID NO:2547, SEQ ID NO:2548, SEQ ID NO:2549,SEQ ID NO:2550, SEQ ID NO:2551, SEQ ID NO:2552, SEQ ID NO:2553, SEQ IDNO:2554, SEQ ID NO:2555, SEQ ID NO:2556, SEQ ID NO:2557, SEQ ID NO:2558,SEQ ID NO:2559, SEQ ID NO:2560, SEQ ID NO:2561, SEQ ID NO:2562, SEQ IDNO:2563, SEQ ID NO:2564, SEQ ID NO:2565, SEQ ID NO:2566, SEQ ID NO:2567,SEQ ID NO:2568, SEQ ID NO:2569, SEQ ID NO:2570, SEQ ID NO:2571, SEQ IDNO:2572, SEQ ID NO:2573, SEQ ID NO:2574, SEQ ID NO:2575, SEQ ID NO:2576,SEQ ID NO:2577, SEQ ID NO:2578, SEQ ID NO:2579, SEQ ID NO:2580, SEQ IDNO:2581, SEQ ID NO:2582, SEQ ID NO:2583, SEQ ID NO:2584, SEQ ID NO:2585,SEQ ID NO:2586, SEQ ID NO:2587, SEQ ID NO:2588, SEQ ID NO:2589, SEQ IDNO:2590, SEQ ID NO:2591, SEQ ID NO:2592, SEQ ID NO:2593, SEQ ID NO:2594,SEQ ID NO:2595, SEQ ID NO:2596, SEQ ID NO:2597, SEQ ID NO:2598, SEQ IDNO:2599, SEQ ID NO:2600, SEQ ID NO:2601, SEQ ID NO:2602, SEQ ID NO:2603,SEQ ID NO:2604, SEQ ID NO:2605, SEQ ID NO:2606, SEQ ID NO:2607, SEQ IDNO:2608, SEQ ID NO:2609, SEQ ID NO:2610, SEQ ID NO:2611, SEQ ID NO:2612,SEQ ID NO:2613, SEQ ID NO:2614, SEQ ID NO:2615, SEQ ID NO:2616, SEQ IDNO:2617, SEQ ID NO:2618, SEQ ID NO:2619, SEQ ID NO:2620, SEQ ID NO:2621,SEQ ID NO:2622, SEQ ID NO:2623, SEQ ID NO:2624, SEQ ID NO:2625, SEQ IDNO:2626, SEQ ID NO:2925, SEQ ID NO:2926, SEQ ID NO:2927, SEQ ID NO:2928,SEQ ID NO:2929, SEQ ID NO:2930, SEQ ID NO:2932, SEQ ID NO:2933, SEQ IDNO:2935, SEQ ID NO:2936, SEQ ID NO:2937, SEQ ID NO:2938, SEQ ID NO:2939,SEQ ID NO:2941, SEQ ID NO:2942, SEQ ID NO:2943, SEQ ID NO:2945, SEQ IDNO:2946, SEQ ID NO:2947, SEQ ID NO:2948, SEQ ID NO:2949, SEQ ID NO:2950,SEQ ID NO:2951, SEQ ID NO:2952, SEQ ID NO:2953, SEQ ID NO:2954, SEQ IDNO:2955, SEQ ID NO:2956, SEQ ID NO:2957, SEQ ID NO:2959, SEQ ID NO:2960,SEQ ID NO:2961, SEQ ID NO:2962, SEQ ID NO:2963, SEQ ID NO:2964, SEQ IDNO:2965, SEQ ID NO:2966, SEQ ID NO:2967, SEQ ID NO:2968, SEQ ID NO:2969,SEQ ID NO:2970, SEQ ID NO:2971, SEQ ID NO:2972, SEQ ID NO:2973, SEQ IDNO:2974, SEQ ID NO:2975, SEQ ID NO:2976, SEQ ID NO:2977, SEQ ID NO:2978,SEQ ID NO:2979, SEQ ID NO:2980, SEQ ID NO:2981, SEQ ID NO:2982, SEQ IDNO:2983, SEQ ID NO:2984, SEQ ID NO:2985, SEQ ID NO:2986, SEQ ID NO:2987,SEQ ID NO:2988, SEQ ID NO:2989, SEQ ID NO:2990, SEQ ID NO:2991, SEQ IDNO:2992, SEQ ID NO:2993, SEQ ID NO:2994, SEQ ID NO:2995, SEQ ID NO:2996,SEQ ID NO:2997, SEQ ID NO:2998, SEQ ID NO:2999, SEQ ID NO:3000, SEQ IDNO:3001, SEQ ID NO:3002, SEQ ID NO:3003, SEQ ID NO:3004, SEQ ID NO:3005,SEQ ID NO:3006, SEQ ID NO:3007, SEQ ID NO:3008, SEQ ID NO:3009, SEQ IDNO:3010, SEQ ID NO:3011, SEQ ID NO:3012, SEQ ID NO:3013, SEQ ID NO:3014,SEQ ID NO:3015. In a further variation, the method includes detectingone or more additional proteins expressed by SEQ ID NO:2406, SEQ IDNO:2431, SEQ ID NO:2471, SEQ ID NO:2472, SEQ ID NO:2473, SEQ ID NO:2474,SEQ ID NO:2475, SEQ ID NO:2476, SEQ ID NO:2477, SEQ ID NO:2484, SEQ IDNO:2487, SEQ ID NO:2489, SEQ ID NO:2490, SEQ ID NO:2498, SEQ ID NO:2499,SEQ ID NO:2500, SEQ ID NO:2501, SEQ ID NO:2522, SEQ ID NO:2523, SEQ IDNO:2524, SEQ ID NO:2525, SEQ ID NO:2526, SEQ ID NO:2527. In stillanother variation, one or more proteins may be selected from SEQ IDNO:2400, SEQ ID NO:2401, SEQ ID NO:2402, SEQ ID NO:2403, SEQ ID NO:2404,SEQ ID NO:2405, SEQ ID NO:2407, SEQ ID NO:2408, SEQ ID NO:2409, SEQ IDNO:2410, SEQ ID NO:2411, SEQ ID NO:2412, SEQ ID NO:2413, SEQ ID NO:2414,SEQ ID NO:2415, SEQ ID NO:2416, SEQ ID NO:2417, SEQ ID NO:2418, SEQ IDNO:2419, SEQ ID NO:2420, SEQ ID NO:2421, SEQ ID NO:2422, SEQ ID NO:2423,SEQ ID NO:2424, SEQ ID NO:2425, SEQ ID NO:2426, SEQ ID NO:2427, SEQ IDNO:2428, SEQ ID NO:2429, SEQ ID NO:2430, SEQ ID NO:2432, SEQ ID NO:2433,SEQ ID NO:2434, SEQ ID NO:2435, SEQ ID NO:2436, SEQ ID NO:2437, SEQ IDNO:2438, SEQ ID NO:2439, SEQ ID NO:2440, SEQ ID NO:2441, SEQ ID NO:2442,SEQ ID NO:2443, SEQ ID NO:2444, SEQ ID NO:2445, SEQ ID NO:2446, SEQ IDNO:2447, SEQ ID NO:2448, SEQ ID NO:2449, SEQ ID NO:2450, SEQ ID NO:2451,SEQ ID NO:2452, SEQ ID NO:2453, SEQ ID NO:2454, SEQ ID NO:2455, SEQ IDNO:2456, SEQ ID NO:2457, SEQ ID NO:2458, SEQ ID NO:2459, SEQ ID NO:2460,SEQ ID NO:2461, SEQ ID NO:2462, SEQ ID NO:2463, SEQ ID NO:2464, SEQ IDNO:2465, SEQ ID NO:2466, SEQ ID NO:2467, SEQ ID NO:2468, SEQ ID NO:2469,SEQ ID NO:2470, SEQ ID NO:2478, SEQ ID NO:2479, SEQ ID NO:2480, SEQ IDNO:2481, SEQ ID NO:2482, SEQ ID NO:2483, SEQ ID NO:2485, SEQ ID NO:2486,SEQ ID NO:2488, SEQ ID NO:2491, SEQ ID NO:2492, SEQ ID NO:2493, SEQ IDNO:2494, SEQ ID NO:2495, SEQ ID NO:2496, SEQ ID NO:2497, SEQ ID NO:2502,SEQ ID NO:2503, SEQ ID NO:2504, SEQ ID NO:2505, SEQ ID NO:2506, SEQ IDNO:2507, SEQ ID NO:2508, SEQ ID NO:2509, SEQ ID NO:2510, SEQ ID NO:2511,SEQ ID NO:2512, SEQ ID NO:2513, SEQ ID NO:2514, SEQ ID NO:2515, SEQ IDNO:2516, SEQ ID NO:2517, SEQ ID NO:2518, SEQ ID NO:2519, SEQ ID NO:2520,SEQ ID NO:2521, SEQ ID NO:2528, SEQ ID NO:2529, SEQ ID NO:2530, SEQ IDNO:2531, SEQ ID NO:2532, SEQ ID NO:2533, SEQ ID NO:2534, SEQ ID NO:2535,SEQ ID NO:2536, SEQ ID NO:2537, SEQ ID NO:2538, SEQ ID NO:2539, SEQ IDNO:2540, SEQ ID NO:2541, SEQ ID NO:2542, SEQ ID NO:2543, SEQ ID NO:2544,SEQ ID NO:2545, SEQ ID NO:2546, SEQ ID NO:2547, SEQ ID NO:2548, SEQ IDNO:2549, SEQ ID NO:2550, SEQ ID NO:2551, SEQ ID NO:2552, SEQ ID NO:2553,SEQ ID NO:2554, SEQ ID NO:2555, SEQ ID NO:2556, SEQ ID NO:2557, SEQ IDNO:2558, SEQ ID NO:2559, SEQ ID NO:2560, SEQ ID NO:2561, SEQ ID NO:2562,SEQ ID NO:2563, SEQ ID NO:2564, SEQ ID NO:2565, SEQ ID NO:2566, SEQ IDNO:2567, SEQ ID NO:2568, SEQ ID NO:2569, SEQ ID NO:2570, SEQ ID NO:2571,SEQ ID NO:2572, SEQ ID NO:2573, SEQ ID NO:2574, SEQ ID NO:2575, SEQ IDNO:2576, SEQ ID NO:2577, SEQ ID NO:2578, SEQ ID NO:2579, SEQ ID NO:2580,SEQ ID NO:2581, SEQ ID NO:2582, SEQ ID NO:2583, SEQ ID NO:2584, SEQ IDNO:2585, SEQ ID NO:2586, SEQ ID NO:2587, SEQ ID NO:2588, SEQ ID NO:2589,SEQ ID NO:2590, SEQ ID NO:2591, SEQ ID NO:2592, SEQ ID NO:2593, SEQ IDNO:2594, SEQ ID NO:2595, SEQ ID NO:2596, SEQ ID NO:2597, SEQ ID NO:2598,SEQ ID NO:2599, SEQ ID NO:2600, SEQ ID NO:2601, SEQ ID NO:2602, SEQ IDNO:2603, SEQ ID NO:2604, SEQ ID NO:2605, SEQ ID NO:2606, SEQ ID NO:2607,SEQ ID NO:2608, SEQ ID NO:2609, SEQ ID NO:2610, SEQ ID NO:2611, SEQ IDNO:2612, SEQ ID NO:2613, SEQ ID NO:2614, SEQ ID NO:2615, SEQ ID NO:2616,SEQ ID NO:2617, SEQ ID NO:2618, SEQ ID NO:2619, SEQ ID NO:2620, SEQ IDNO:2621, SEQ ID NO:2622, SEQ ID NO:2623, SEQ ID NO:2624, SEQ ID NO:2625,SEQ ID NO:2626, SEQ ID NO:2925, SEQ ID NO:2926, SEQ ID NO:2927, SEQ IDNO:2928, SEQ ID NO:2929, SEQ ID NO:2930, SEQ ID NO:2932, SEQ ID NO:2933,SEQ ID NO:2935, SEQ ID NO:2936, SEQ ID NO:2937, SEQ ID NO:2938, SEQ IDNO:2939, SEQ ID NO:2941, SEQ ID NO:2942, SEQ ID NO:2943, SEQ ID NO:2945,SEQ ID NO:2946, SEQ ID NO:2947, SEQ ID NO:2948, SEQ ID NO:2949, SEQ IDNO:2950, SEQ ID NO:2951, SEQ ID NO:2952, SEQ ID NO:2953, SEQ ID NO:2954,SEQ ID NO:2955, SEQ ID NO:2956, SEQ ID NO:2957, SEQ ID NO:2959, SEQ IDNO:2960, SEQ ID NO:2961, SEQ ID NO:2962, SEQ ID NO:2963, SEQ ID NO:2964,SEQ ID NO:2965, SEQ ID NO:2966, SEQ ID NO:2967, SEQ ID NO:2968, SEQ IDNO:2969, SEQ ID NO:2970, SEQ ID NO:2971, SEQ ID NO:2972, SEQ ID NO:2973,SEQ ID NO:2974, SEQ ID NO:2975, SEQ ID NO:2976, SEQ ID NO:2977, SEQ IDNO:2978, SEQ ID NO:2979, SEQ ID NO:2980, SEQ ID NO:2981, SEQ ID NO:2982,SEQ ID NO:2983, SEQ ID NO:2984, SEQ ID NO:2985, SEQ ID NO:2986, SEQ IDNO:2987, SEQ ID NO:2988, SEQ ID NO:2989, SEQ ID NO:2990, SEQ ID NO:2991,SEQ ID NO:2992, SEQ ID NO:2993, SEQ ID NO:2994, SEQ ID NO:2995, SEQ IDNO:2996, SEQ ID NO:2997, SEQ ID NO:2998, SEQ ID NO:2999, SEQ ID NO:3000,SEQ ID NO:3001, SEQ ID NO:3002, SEQ ID NO:3003, SEQ ID NO:3004, SEQ IDNO:3005, SEQ ID NO:3006, SEQ ID NO:3007, SEQ ID NO:3008, SEQ ID NO:3009,SEQ ID NO:3010, SEQ ID NO:3011, SEQ ID NO:3012, SEQ ID NO:3013, SEQ IDNO:3014, SEQ ID NO:3015, and one or more proteins may be selected fromSEQ ID NO:2406, SEQ ID NO:2431, SEQ ID NO:2471, SEQ ID NO:2472, SEQ IDNO:2473, SEQ ID NO:2474, SEQ ID NO:2475, SEQ ID NO:2476, SEQ ID NO:2477,SEQ ID NO:2484, SEQ ID NO:2487, SEQ ID NO:2489, SEQ ID NO:2490, SEQ IDNO:2498, SEQ ID NO:2499, SEQ ID NO:2500, SEQ ID NO:2501, SEQ ID NO:2522,SEQ ID NO:2523, SEQ ID NO:2524, SEQ ID NO:2525, SEQ ID NO:2526, SEQ IDNO:2527.

In another aspect, the method of diagnosing or monitoring cardiactransplant rejection in a patient includes detecting the expressionlevel of one or more genes in the patient to diagnose or monitor cardiactransplant rejection in the patient by measuring one or more proteinsexpressed by the one or more genes. The one or more proteins may includean amino acid sequence selected from SEQ ID NO:2400, SEQ ID NO:2401, SEQID NO:2402, SEQ ID NO:2403, SEQ ID NO:2404, SEQ ID NO:2405, SEQ IDNO:2407, SEQ ID NO:2408, SEQ ID NO:2409, SEQ ID NO:2410, SEQ ID NO:2411,SEQ ID NO:2412, SEQ ID NO:2413, SEQ ID NO:2414, SEQ ID NO:2415, SEQ IDNO:2416, SEQ ID NO:2417, SEQ ID NO:2418, SEQ ID NO:2419, SEQ ID NO:2420,SEQ ID NO:2421, SEQ ID NO:2422, SEQ ID NO:2423, SEQ ID NO:2424, SEQ IDNO:2425, SEQ ID NO:2426, SEQ ID NO:2427, SEQ ID NO:2428, SEQ ID NO:2429,SEQ ID NO:2430, SEQ ID NO:2432, SEQ ID NO:2433, SEQ ID NO:2434, SEQ IDNO:2435, SEQ ID NO:2436, SEQ ID NO:2437, SEQ ID NO:2438, SEQ ID NO:2439,SEQ ID NO:2440, SEQ ID NO:2441, SEQ ID NO:2442, SEQ ID NO:2443, SEQ IDNO:2444, SEQ ID NO:2445, SEQ ID NO:2446, SEQ ID NO:2447, SEQ ID NO:2448,SEQ ID NO:2449, SEQ ID NO:2450, SEQ ID NO:2451, SEQ ID NO:2452, SEQ IDNO:2453, SEQ ID NO:2454, SEQ ID NO:2455, SEQ ID NO:2456, SEQ ID NO:2457,SEQ ID NO:2458, SEQ ID NO:2459, SEQ ID NO:2460, SEQ ID NO:2461, SEQ IDNO:2462, SEQ ID NO:2463, SEQ ID NO:2464, SEQ ID NO:2465, SEQ ID NO:2466,SEQ ID NO:2467, SEQ ID NO:2468, SEQ ID NO:2469, SEQ ID NO:2470, SEQ IDNO:2471, SEQ ID NO:2476, SEQ ID NO:2477, SEQ ID NO:2478, SEQ ID NO:2479,SEQ ID NO:2480, SEQ ID NO:2481, SEQ ID NO:2482, SEQ ID NO:2483, SEQ IDNO:2484, SEQ ID NO:2485, SEQ ID NO:2486, SEQ ID NO:2488, SEQ ID NO:2489,SEQ ID NO:2490, SEQ ID NO:2491, SEQ ID NO:2492, SEQ ID NO:2493, SEQ IDNO:2494, SEQ ID NO:2495, SEQ ID NO:2496, SEQ ID NO:2497, SEQ ID NO:2498,SEQ ID NO:2499, SEQ ID NO:2500, SEQ ID NO:2501, SEQ ID NO:2502, SEQ IDNO:2503, SEQ ID NO:2504, SEQ ID NO:2505, SEQ ID NO:2506, SEQ ID NO:2507,SEQ ID NO:2508, SEQ ID NO:2509, SEQ ID NO:2510, SEQ ID NO:2511, SEQ IDNO:2512, SEQ ID NO:2513, SEQ ID NO:2514, SEQ ID NO:2515, SEQ ID NO:2516,SEQ ID NO:2517, SEQ ID NO:2518, SEQ ID NO:2519, SEQ ID NO:2520, SEQ IDNO:2521, SEQ ID NO:2528, SEQ ID NO:2529, SEQ ID NO:2530, SEQ ID NO:2531,SEQ ID NO:2532, SEQ ID NO:2533, SEQ ID NO:2534, SEQ ID NO:2535, SEQ IDNO:2536, SEQ ID NO:2537, SEQ ID NO:2538, SEQ ID NO:2539, SEQ ID NO:2540,SEQ ID NO:2541, SEQ ID NO:2542, SEQ ID NO:2543, SEQ ID NO:2544, SEQ IDNO:2545, SEQ ID NO:2546, SEQ ID NO:2547, SEQ ID NO:2548, SEQ ID NO:2549,SEQ ID NO:2550, SEQ ID NO:2551, SEQ ID NO:2552, SEQ ID NO:2553, SEQ IDNO:2554, SEQ ID NO:2555, SEQ ID NO:2556, SEQ ID NO:2557, SEQ ID NO:2558,SEQ ID NO:2559, SEQ ID NO:2560, SEQ ID NO:2561, SEQ ID NO:2562, SEQ IDNO:2563, SEQ ID NO:2564, SEQ ID NO:2565, SEQ ID NO:2566, SEQ ID NO:2567,SEQ ID NO:2568, SEQ ID NO:2569, SEQ ID NO:2570, SEQ ID NO:2571, SEQ IDNO:2572, SEQ ID NO:2573, SEQ ID NO:2574, SEQ ID NO:2575, SEQ ID NO:2576,SEQ ID NO:2577, SEQ ID NO:2578, SEQ ID NO:2579, SEQ ID NO:2580, SEQ IDNO:2581, SEQ ID NO:2582, SEQ ID NO:2583, SEQ ID NO:2584, SEQ ID NO:2585,SEQ ID NO:2586, SEQ ID NO:2587, SEQ ID NO:2588, SEQ ID NO:2589, SEQ IDNO:2590, SEQ ID NO:2591, SEQ ID NO:2592, SEQ ID NO:2593, SEQ ID NO:2594,SEQ ID NO:2595, SEQ ID NO:2596, SEQ ID NO:2597, SEQ ID NO:2598, SEQ IDNO:2599, SEQ ID NO:2600, SEQ ID NO:2601, SEQ ID NO:2602, SEQ ID NO:2603,SEQ ID NO:2604, SEQ ID NO:2605, SEQ ID NO:2606, SEQ ID NO:2607, SEQ IDNO:2608, SEQ ID NO:2609, SEQ ID NO:2610, SEQ ID NO:2611, SEQ ID NO:2612,SEQ ID NO:2613, SEQ ID NO:2614, SEQ ID NO:2615, SEQ ID NO:2616, SEQ IDNO:2617, SEQ ID NO:2618, SEQ ID NO:2619, SEQ ID NO:2620, SEQ ID NO:2621,SEQ ID NO:2622, SEQ ID NO:2623, SEQ ID NO:2624, SEQ ID NO:2625, SEQ IDNO:2626. Alternatively, the expression level of the one or more genesmay be detected by measuring one or more proteins expressed by one ormore genes, and one or more proteins expressed by one or more additionalgenes. In one variation, the one or more proteins expressed by the oneor more genes include an amino acid sequence selected from SEQ IDNO:2400, SEQ ID NO:2401, SEQ ID NO:2402, SEQ ID NO:2403, SEQ ID NO:2404,SEQ ID NO:2405, SEQ ID NO:2407, SEQ ID NO:2408, SEQ ID NO:2409, SEQ IDNO:2410, SEQ ID NO:2411, SEQ ID NO:2412, SEQ ID NO:2413, SEQ ID NO:2414,SEQ ID NO:2415, SEQ ID NO:2416, SEQ ID NO:2417, SEQ ID NO:2418, SEQ IDNO:2419, SEQ ID NO:2420, SEQ ID NO:2421, SEQ ID NO:2422, SEQ ID NO:2423,SEQ ID NO:2424, SEQ ID NO:2425, SEQ ID NO:2426, SEQ ID NO:2427, SEQ IDNO:2428, SEQ ID NO:2429, SEQ ID NO:2430, SEQ ID NO:2432, SEQ ID NO:2433,SEQ ID NO:2434, SEQ ID NO:2435, SEQ ID NO:2436, SEQ ID NO:2437, SEQ IDNO:2438, SEQ ID NO:2439, SEQ ID NO:2440, SEQ ID NO:2441, SEQ ID NO:2442,SEQ ID NO:2443, SEQ ID NO:2444, SEQ ID NO:2445, SEQ ID NO:2446, SEQ IDNO:2447, SEQ ID NO:2448, SEQ ID NO:2449, SEQ ID NO:2450, SEQ ID NO:2451,SEQ ID NO:2452, SEQ ID NO:2453, SEQ ID NO:2454, SEQ ID NO:2455, SEQ IDNO:2456, SEQ ID NO:2457, SEQ ID NO:2458, SEQ ID NO:2459, SEQ ID NO:2460,SEQ ID NO:2461, SEQ ID NO:2462, SEQ ID NO:2463, SEQ ID NO:2464, SEQ IDNO:2465, SEQ ID NO:2466, SEQ ID NO:2467, SEQ ID NO:2468, SEQ ID NO:2469,SEQ ID NO:2470, SEQ ID NO:2471, SEQ ID NO:2476, SEQ ID NO:2477, SEQ IDNO:2478, SEQ ID NO:2479, SEQ ID NO:2480, SEQ ID NO:2481, SEQ ID NO:2482,SEQ ID NO:2483, SEQ ID NO:2484, SEQ ID NO:2485, SEQ ID NO:2486, SEQ IDNO:2488, SEQ ID NO:2489, SEQ ID NO:2490, SEQ ID NO:2491, SEQ ID NO:2492,SEQ ID NO:2493, SEQ ID NO:2494, SEQ ID NO:2495, SEQ ID NO:2496, SEQ IDNO:2497, SEQ ID NO:2498, SEQ ID NO:2499, SEQ ID NO:2500, SEQ ID NO:2501,SEQ ID NO:2502, SEQ ID NO:2503, SEQ ID NO:2504, SEQ ID NO:2505, SEQ IDNO:2506, SEQ ID NO:2507, SEQ ID NO:2508, SEQ ID NO:2509, SEQ ID NO:2510,SEQ ID NO:2511, SEQ ID NO:2512, SEQ ID NO:2513, SEQ ID NO:2514, SEQ IDNO:2515, SEQ ID NO:2516, SEQ ID NO:2517, SEQ ID NO:2518, SEQ ID NO:2519,SEQ ID NO:2520, SEQ ID NO:2521, SEQ ID NO:2528, SEQ ID NO:2529, SEQ IDNO:2530, SEQ ID NO:2531, SEQ ID NO:2532, SEQ ID NO:2533, SEQ ID NO:2534,SEQ ID NO:2535, SEQ ID NO:2536, SEQ ID NO:2537, SEQ ID NO:2538, SEQ IDNO:2539, SEQ ID NO:2540, SEQ ID NO:2541, SEQ ID NO:2542, SEQ ID NO:2543,SEQ ID NO:2544, SEQ ID NO:2545, SEQ ID NO:2546, SEQ ID NO:2547, SEQ IDNO:2548, SEQ ID NO:2549, SEQ ID NO:2550, SEQ ID NO:2551, SEQ ID NO:2552,SEQ ID NO:2553, SEQ ID NO:2554, SEQ ID NO:2555, SEQ ID NO:2556, SEQ IDNO:2557, SEQ ID NO:2558, SEQ ID NO:2559, SEQ ID NO:2560, SEQ ID NO:2561,SEQ ID NO:2562, SEQ ID NO:2563, SEQ ID NO:2564, SEQ ID NO:2565, SEQ IDNO:2566, SEQ ID NO:2567, SEQ ID NO:2568, SEQ ID NO:2569, SEQ ID NO:2570,SEQ ID NO:2571, SEQ ID NO:2572, SEQ ID NO:2573, SEQ ID NO:2574, SEQ IDNO:2575, SEQ ID NO:2576, SEQ ID NO:2577, SEQ ID NO:2578, SEQ ID NO:2579,SEQ ID NO:2580, SEQ ID NO:2581, SEQ ID NO:2582, SEQ ID NO:2583, SEQ IDNO:2584, SEQ ID NO:2585, SEQ ID NO:2586, SEQ ID NO:2587, SEQ ID NO:2588,SEQ ID NO:2589, SEQ ID NO:2590, SEQ ID NO:2591, SEQ ID NO:2592, SEQ IDNO:2593, SEQ ID NO:2594, SEQ ID NO:2595, SEQ ID NO:2596, SEQ ID NO:2597,SEQ ID NO:2598, SEQ ID NO:2599, SEQ ID NO:2600, SEQ ID NO:2601, SEQ IDNO:2602, SEQ ID NO:2603, SEQ ID NO:2604, SEQ ID NO:2605, SEQ ID NO:2606,SEQ ID NO:2607, SEQ ID NO:2608, SEQ ID NO:2609, SEQ ID NO:2610, SEQ IDNO:2611, SEQ ID NO:2612, SEQ ID NO:2613, SEQ ID NO:2614, SEQ ID NO:2615,SEQ ID NO:2616, SEQ ID NO:2617, SEQ ID NO:2618, SEQ ID NO:2619, SEQ IDNO:2620, SEQ ID NO:2621, SEQ ID NO:2622, SEQ ID NO:2623, SEQ ID NO:2624,SEQ ID NO:2625, SEQ ID NO:2626, and the one or more protein expressed bythe one or more additional genes include an amino acid sequence selectedfrom the group consisting of SEQ ID NO:2406, SEQ ID NO:2431, SEQ IDNO:2472, SEQ ID NO:2473, SEQ ID NO:2474, SEQ ID NO:2475, SEQ ID NO:2487,SEQ ID NO:2522, SEQ ID NO:2523, SEQ ID NO:2524, SEQ ID NO:2525, SEQ IDNO:2526, SEQ ID NO:2527.

In another aspect, the method of diagnosing or monitoring kidneytransplant rejection in a patient includes detecting the expressionlevel of one or more genes in the patient to diagnose or monitor kidneytransplant rejection in the patient by measuring one or more proteinsencoded by the one or more genes. In one variation, the one or moreproteins include an amino acid sequence selected from SEQ ID NO:2400,SEQ ID NO:2401, SEQ ID NO:2402, SEQ ID NO:2403, SEQ ID NO:2404, SEQ IDNO:2405, SEQ ID NO:2406, SEQ ID NO:2407, SEQ ID NO:2408, SEQ ID NO:2409,SEQ ID NO:2410, SEQ ID NO:2411, SEQ ID NO:2412, SEQ ID NO:2413, SEQ IDNO:2414, SEQ ID NO:2415, SEQ ID NO:2416, SEQ ID NO:2417, SEQ ID NO:2418,SEQ ID NO:2419, SEQ ID NO:2420, SEQ ID NO:2421, SEQ ID NO:2422, SEQ IDNO:2423, SEQ ID NO:2424, SEQ ID NO:2425, SEQ ID NO:2426, SEQ ID NO:2427,SEQ ID NO:2428, SEQ ID NO:2429, SEQ ID NO:2430, SEQ ID NO:2432, SEQ IDNO:2433, SEQ ID NO:2434, SEQ ID NO:2435, SEQ ID NO:2436, SEQ ID NO:2437,SEQ ID NO:2438, SEQ ID NO:2439, SEQ ID NO:2440, SEQ ID NO:2441, SEQ IDNO:2442, SEQ ID NO:2443, SEQ ID NO:2444, SEQ ID NO:2445, SEQ ID NO:2446,SEQ ID NO:2447, SEQ ID NO:2448, SEQ ID NO:2449, SEQ ID NO:2450, SEQ IDNO:2451, SEQ ID NO:2452, SEQ ID NO:2453, SEQ ID NO:2454, SEQ ID NO:2455,SEQ ID NO:2456, SEQ ID NO:2457, SEQ ID NO:2458, SEQ ID NO:2459, SEQ IDNO:2460, SEQ ID NO:2461, SEQ ID NO:2462, SEQ ID NO:2463, SEQ ID NO:2464,SEQ ID NO:2465, SEQ ID NO:2466, SEQ ID NO:2467, SEQ ID NO:2468, SEQ IDNO:2469, SEQ ID NO:2470, SEQ ID NO:2474, SEQ ID NO:2478, SEQ ID NO:2479,SEQ ID NO:2480, SEQ ID NO:2481, SEQ ID NO:2482, SEQ ID NO:2483, SEQ IDNO:2485, SEQ ID NO:2486, SEQ ID NO:2487, SEQ ID NO:2488, SEQ ID NO:2491,SEQ ID NO:2492, SEQ ID NO:2493, SEQ ID NO:2494, SEQ ID NO:2495, SEQ IDNO:2496, SEQ ID NO:2497, SEQ ID NO:2502, SEQ ID NO:2503, SEQ ID NO:2504,SEQ ID NO:2505, SEQ ID NO:2506, SEQ ID NO:2507, SEQ ID NO:2508, SEQ IDNO:2509, SEQ ID NO:2510, SEQ ID NO:2511, SEQ ID NO:2512, SEQ ID NO:2513,SEQ ID NO:2514, SEQ ID NO:2515, SEQ ID NO:2516, SEQ ID NO:2517, SEQ IDNO:2518, SEQ ID NO:2519, SEQ ID NO:2520, SEQ ID NO:2521, SEQ ID NO:2528,SEQ ID NO:2529, SEQ ID NO:2530, SEQ ID NO:2531, SEQ ID NO:2532, SEQ IDNO:2533, SEQ ID NO:2534, SEQ ID NO:2535, SEQ ID NO:2536, SEQ ID NO:2537,SEQ ID NO:2538, SEQ ID NO:2539, SEQ ID NO:2540, SEQ ID NO:2541, SEQ IDNO:2542, SEQ ID NO:2543, SEQ ID NO:2544, SEQ ID NO:2545, SEQ ID NO:2546,SEQ ID NO:2547, SEQ ID NO:2548, SEQ ID NO:2549, SEQ ID NO:2550, SEQ IDNO:2551, SEQ ID NO:2552, SEQ ID NO:2553, SEQ ID NO:2554, SEQ ID NO:2555,SEQ ID NO:2556, SEQ ID NO:2557, SEQ ID NO:2558, SEQ ID NO:2559, SEQ IDNO:2560, SEQ ID NO:2561, SEQ ID NO:2562, SEQ ID NO:2563, SEQ ID NO:2564,SEQ ID NO:2565, SEQ ID NO:2566, SEQ ID NO:2567, SEQ ID NO:2568, SEQ IDNO:2569, SEQ ID NO:2570, SEQ ID NO:2571, SEQ ID NO:2572, SEQ ID NO:2573,SEQ ID NO:2574, SEQ ID NO:2575, SEQ ID NO:2576, SEQ ID NO:2577, SEQ IDNO:2578, SEQ ID NO:2579, SEQ ID NO:2580, SEQ ID NO:2581, SEQ ID NO:2582,SEQ ID NO:2583, SEQ ID NO:2584, SEQ ID NO:2585, SEQ ID NO:2586, SEQ IDNO:2587, SEQ ID NO:2588, SEQ ID NO:2589, SEQ ID NO:2590, SEQ ID NO:2591,SEQ ID NO:2592, SEQ ID NO:2593, SEQ ID NO:2594, SEQ ID NO:2595, SEQ IDNO:2596, SEQ ID NO:2597, SEQ ID NO:2598, SEQ ID NO:2599, SEQ ID NO:2600,SEQ ID NO:2601, SEQ ID NO:2602, SEQ ID NO:2603, SEQ ID NO:2604, SEQ IDNO:2605, SEQ ID NO:2606, SEQ ID NO:2607, SEQ ID NO:2608, SEQ ID NO:2609,SEQ ID NO:2610, SEQ ID NO:2611, SEQ ID NO:2612, SEQ ID NO:2613, SEQ IDNO:2614, SEQ ID NO:2615, SEQ ID NO:2616, SEQ ID NO:2617, SEQ ID NO:2618,SEQ ID NO:2619, SEQ ID NO:2620, SEQ ID NO:2621, SEQ ID NO:2622, SEQ IDNO:2623, SEQ ID NO:2624, SEQ ID NO:2625, SEQ ID NO:2626, SEQ ID NO:2925,SEQ ID NO:2926, SEQ ID NO:2927, SEQ ID NO:2928, SEQ ID NO:2929, SEQ IDNO:2930, SEQ ID NO:2932, SEQ ID NO:2933, SEQ ID NO:2935, SEQ ID NO:2936,SEQ ID NO:2937, SEQ ID NO:2938, SEQ ID NO:2939, SEQ ID NO:2941, SEQ IDNO:2942, SEQ ID NO:2943, SEQ ID NO:2945, SEQ ID NO:2946, SEQ ID NO:2947,SEQ ID NO:2948, SEQ ID NO:2949, SEQ ID NO:2950, SEQ ID NO:2951, SEQ IDNO:2952, SEQ ID NO:2953, SEQ ID NO:2954, SEQ ID NO:2955, SEQ ID NO:2956,SEQ ID NO:2957, SEQ ID NO:2959, SEQ ID NO:2960, SEQ ID NO:2961, SEQ IDNO:2962, SEQ ID NO:2963, SEQ ID NO:2964, SEQ ID NO:2965, SEQ ID NO:2966,SEQ ID NO:2967, SEQ ID NO:2968, SEQ ID NO:2969, SEQ ID NO:2970, SEQ IDNO:2971, SEQ ID NO:2972, SEQ ID NO:2973, SEQ ID NO:2974, SEQ ID NO:2975,SEQ ID NO:2976, SEQ ID NO:2977, SEQ ID NO:2978, SEQ ID NO:2979, SEQ IDNO:2980, SEQ ID NO:2981, SEQ ID NO:2982, SEQ ID NO:2983, SEQ ID NO:2984,SEQ ID NO:2985, SEQ ID NO:2986, SEQ ID NO:2987, SEQ ID NO:2988, SEQ IDNO:2989, SEQ ID NO:2990, SEQ ID NO:2991, SEQ ID NO:2992, SEQ ID NO:2993,SEQ ID NO:2994, SEQ ID NO:2995, SEQ ID NO:2996, SEQ ID NO:2997, SEQ IDNO:2998, SEQ ID NO:2999, SEQ ID NO:3000, SEQ ID NO:3001, SEQ ID NO:3002,SEQ ID NO:3003, SEQ ID NO:3004, SEQ ID NO:3005, SEQ ID NO:3006, SEQ IDNO:3007, SEQ ID NO:3008, SEQ ID NO:3009, SEQ ID NO:3010, SEQ ID NO:3011,SEQ ID NO:3012, SEQ ID NO:3013, SEQ ID NO:3014, SEQ ID NO:3015. Inanother variation, the method includes detecting the expression level ofone or more additional genes by measuring one or more proteins expressedby the one or more additional genes. The one or more proteins expressedby the one or more genes comprises an amino acid sequence selected fromSEQ ID NO:2400, SEQ ID NO:2401, SEQ ID NO:2402, SEQ ID NO:2403, SEQ IDNO:2404, SEQ ID NO:2405, SEQ ID NO:2406, SEQ ID NO:2407, SEQ ID NO:2408,SEQ ID NO:2409, SEQ ID NO:2410, SEQ ID NO:2411, SEQ ID NO:2412, SEQ IDNO:2413, SEQ ID NO:2414, SEQ ID NO:2415, SEQ ID NO:2416, SEQ ID NO:2417,SEQ ID NO:2418, SEQ ID NO:2419, SEQ ID NO:2420, SEQ ID NO:2421, SEQ IDNO:2422, SEQ ID NO:2423, SEQ ID NO:2424, SEQ ID NO:2425, SEQ ID NO:2426,SEQ ID NO:2427, SEQ ID NO:2428, SEQ ID NO:2429, SEQ ID NO:2430, SEQ IDNO:2432, SEQ ID NO:2433, SEQ ID NO:2434, SEQ ID NO:2435, SEQ ID NO:2436,SEQ ID NO:2437, SEQ ID NO:2438, SEQ ID NO:2439, SEQ ID NO:2440, SEQ IDNO:2441, SEQ ID NO:2442, SEQ ID NO:2443, SEQ ID NO:2444, SEQ ID NO:2445,SEQ ID NO:2446, SEQ ID NO:2447, SEQ ID NO:2448, SEQ ID NO:2449, SEQ IDNO:2450, SEQ ID NO:2451, SEQ ID NO:2452, SEQ ID NO:2453, SEQ ID NO:2454,SEQ ID NO:2455, SEQ ID NO:2456, SEQ ID NO:2457, SEQ ID NO:2458, SEQ IDNO:2459, SEQ ID NO:2460, SEQ ID NO:2461, SEQ ID NO:2462, SEQ ID NO:2463,SEQ ID NO:2464, SEQ ID NO:2465, SEQ ID NO:2466, SEQ ID NO:2467, SEQ IDNO:2468, SEQ ID NO:2469, SEQ ID NO:2470, SEQ ID NO:2474, SEQ ID NO:2478,SEQ ID NO:2479, SEQ ID NO:2480, SEQ ID NO:2481, SEQ ID NO:2482, SEQ IDNO:2483, SEQ ID NO:2485, SEQ ID NO:2486, SEQ ID NO:2487, SEQ ID NO:2488,SEQ ID NO:2491, SEQ ID NO:2492, SEQ ID NO:2493, SEQ ID NO:2494, SEQ IDNO:2495, SEQ ID NO:2496, SEQ ID NO:2497, SEQ ID NO:2502, SEQ ID NO:2503,SEQ ID NO:2504, SEQ ID NO:2505, SEQ ID NO:2506, SEQ ID NO:2507, SEQ IDNO:2508, SEQ ID NO:2509, SEQ ID NO:2510, SEQ ID NO:2511, SEQ ID NO:2512,SEQ ID NO:2513, SEQ ID NO:2514, SEQ ID NO:2515, SEQ ID NO:2516, SEQ IDNO:2517, SEQ ID NO:2518, SEQ ID NO:2519, SEQ ID NO:2520, SEQ ID NO:2521,SEQ ID NO:2528, SEQ ID NO:2529, SEQ ID NO:2530, SEQ ID NO:2531, SEQ IDNO:2532, SEQ ID NO:2533, SEQ ID NO:2534, SEQ ID NO:2535, SEQ ID NO:2536,SEQ ID NO:2537, SEQ ID NO:2538, SEQ ID NO:2539, SEQ ID NO:2540, SEQ IDNO:2541, SEQ ID NO:2542, SEQ ID NO:2543, SEQ ID NO:2544, SEQ ID NO:2545,SEQ ID NO:2546, SEQ ID NO:2547, SEQ ID NO:2548, SEQ ID NO:2549, SEQ IDNO:2550, SEQ ID NO:2551, SEQ ID NO:2552, SEQ ID NO:2553, SEQ ID NO:2554,SEQ ID NO:2555, SEQ ID NO:2556, SEQ ID NO:2557, SEQ ID NO:2558, SEQ IDNO:2559, SEQ ID NO:2560, SEQ ID NO:2561, SEQ ID NO:2562, SEQ ID NO:2563,SEQ ID NO:2564, SEQ ID NO:2565, SEQ ID NO:2566, SEQ ID NO:2567, SEQ IDNO:2568, SEQ ID NO:2569, SEQ ID NO:2570, SEQ ID NO:2571, SEQ ID NO:2572,SEQ ID NO:2573, SEQ ID NO:2574, SEQ ID NO:2575, SEQ ID NO:2576, SEQ IDNO:2577, SEQ ID NO:2578, SEQ ID NO:2579, SEQ ID NO:2580, SEQ ID NO:2581,SEQ ID NO:2582, SEQ ID NO:2583, SEQ ID NO:2584, SEQ ID NO:2585, SEQ IDNO:2586, SEQ ID NO:2587, SEQ ID NO:2588, SEQ ID NO:2589, SEQ ID NO:2590,SEQ ID NO:2591, SEQ ID NO:2592, SEQ ID NO:2593, SEQ ID NO:2594, SEQ IDNO:2595, SEQ ID NO:2596, SEQ ID NO:2597, SEQ ID NO:2598, SEQ ID NO:2599,SEQ ID NO:2600, SEQ ID NO:2601, SEQ ID NO:2602, SEQ ID NO:2603, SEQ IDNO:2604, SEQ ID NO:2605, SEQ ID NO:2606, SEQ ID NO:2607, SEQ ID NO:2608,SEQ ID NO:2609, SEQ ID NO:2610, SEQ ID NO:2611, SEQ ID NO:2612, SEQ IDNO:2613, SEQ ID NO:2614, SEQ ID NO:2615, SEQ ID NO:2616, SEQ ID NO:2617,SEQ ID NO:2618, SEQ ID NO:2619, SEQ ID NO:2620, SEQ ID NO:2621, SEQ IDNO:2622, SEQ ID NO:2623, SEQ ID NO:2624, SEQ ID NO:2625, SEQ ID NO:2626,SEQ ID NO:2925, SEQ ID NO:2926, SEQ ID NO:2927, SEQ ID NO:2928, SEQ IDNO:2929, SEQ ID NO:2930, SEQ ID NO:2932, SEQ ID NO:2933, SEQ ID NO:2935,SEQ ID NO:2936, SEQ ID NO:2937, SEQ ID NO:2938, SEQ ID NO:2939, SEQ IDNO:2941, SEQ ID NO:2942, SEQ ID NO:2943, SEQ ID NO:2945, SEQ ID NO:2946,SEQ ID NO:2947, SEQ ID NO:2948, SEQ ID NO:2949, SEQ ID NO:2950, SEQ IDNO:2951, SEQ ID NO:2952, SEQ ID NO:2953, SEQ ID NO:2954, SEQ ID NO:2955,SEQ ID NO:2956, SEQ ID NO:2957, SEQ ID NO:2959, SEQ ID NO:2960, SEQ IDNO:2961, SEQ ID NO:2962, SEQ ID NO:2963, SEQ ID NO:2964, SEQ ID NO:2965,SEQ ID NO:2966, SEQ ID NO:2967, SEQ ID NO:2968, SEQ ID NO:2969, SEQ IDNO:2970, SEQ ID NO:2971, SEQ ID NO:2972, SEQ ID NO:2973, SEQ ID NO:2974,SEQ ID NO:2975, SEQ ID NO:2976, SEQ ID NO:2977, SEQ ID NO:2978, SEQ IDNO:2979, SEQ ID NO:2980, SEQ ID NO:2981, SEQ ID NO:2982, SEQ ID NO:2983,SEQ ID NO:2984, SEQ ID NO:2985, SEQ ID NO:2986, SEQ ID NO:2987, SEQ IDNO:2988, SEQ ID NO:2989, SEQ ID NO:2990, SEQ ID NO:2991, SEQ ID NO:2992,SEQ ID NO:2993, SEQ ID NO:2994, SEQ ID NO:2995, SEQ ID NO:2996, SEQ IDNO:2997, SEQ ID NO:2998, SEQ ID NO:2999, SEQ ID NO:3000, SEQ ID NO:3001,SEQ ID NO:3002, SEQ ID NO:3003, SEQ ID NO:3004, SEQ ID NO:3005, SEQ IDNO:3006, SEQ ID NO:3007, SEQ ID NO:3008, SEQ ID NO:3009, SEQ ID NO:3010,SEQ ID NO:3011, SEQ ID NO:3012, SEQ ID NO:3013, SEQ ID NO:3014, SEQ IDNO:3015, and the one or more proteins expressed by the one or moreadditional genes may include an amino acid sequence selected from SEQ IDNO:2431, SEQ ID NO:2471, SEQ ID NO:2472, SEQ ID NO:2473, SEQ ID NO:2475,SEQ ID NO:2476, SEQ ID NO:2477, SEQ ID NO:2484, SEQ ID NO:2489, SEQ IDNO:2490, SEQ ID NO:2498, SEQ ID NO:2499, SEQ ID NO:2500, SEQ ID NO:2501,SEQ ID NO:2522, SEQ ID NO:2523, SEQ ID NO:2524, SEQ ID NO:2525, SEQ IDNO:2526, SEQ ID NO:2527.

Protein detection may be accomplished by measuring serum. In anothervariation, the protein is a cell surface protein. In a furthervariation, the measuring includes using a fluorescent activated cellsorter.

In another aspect, the invention is directed to a substantially purifiedoligonucleotide having the nucleotide sequence selected from SEQ IDNO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7,SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO 16, SEQ ID NO:17, SEQ IDNO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ IDNO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ IDNO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ IDNO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ IDNO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ IDNO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ IDNO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ IDNO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ IDNO:58, SEQ ID NO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ IDNO:63, SEQ ID NO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, SEQ IDNO:68, SEQ ID NO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ ID NO:72, SEQ IDNO:73, SEQ ID NO:74, SEQ ID NO:75, SEQ ID NO:76, SEQ ID NO:77, SEQ IDNO:78, SEQ ID NO:79, SEQ ID NO:80, SEQ ID NO:81, SEQ ID NO:82, SEQ IDNO:83, SEQ ID NO:84, SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87, SEQ IDNO:88, SEQ ID NO:89, SEQ ID NO:90, SEQ ID NO:91, SEQ ID NO:92, SEQ IDNO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ IDNO:98, SEQ ID NO:99, SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ IDNO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, SEQID NO:108, SEQ ID NO:109, SEQ ID NO:110, SEQ ID NO:111, SEQ ID NO:112,SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ IDNO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQID NO:122, SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126,SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ IDNO:131, SEQ ID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQ ID NO:135, SEQID NO:136, SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139, SEQ ID NO:140,SEQ ID NO:141, SEQ ID NO:142, SEQ ID NO:143, SEQ ID NO:144, SEQ IDNO:145, SEQ ID NO:146, SEQ ID NO:147, SEQ ID NO:148, SEQ ID NO:149, SEQID NO:150, SEQ ID NO:151, SEQ ID NO:152, SEQ ID NO:153, SEQ ID NO:154,SEQ ID NO:155, SEQ ID NO:156, SEQ ID NO:157, SEQ ID NO:158, SEQ IDNO:159, SEQ ID NO:160, SEQ ID NO:161, SEQ ID NO:162, SEQ ID NO:163, SEQID NO:164, SEQ ID NO:165, SEQ ID NO:166, SEQ ID NO:167, SEQ ID NO:168,SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171, SEQ ID NO:172, SEQ IDNO:173, SEQ ID NO:174, SEQ ID NO:175, SEQ ID NO:176, SEQ ID NO:177, SEQID NO:178, SEQ ID NO:179, SEQ ID NO:180, SEQ ID NO:181, SEQ ID NO:182,SEQ ID NO:183, SEQ ID NO:184, SEQ ID NO:185, SEQ ID NO:186, SEQ IDNO:187, SEQ ID NO:188, SEQ ID NO:189, SEQ ID NO:190, SEQ ID NO:191, SEQID NO:192, SEQ ID NO:193, SEQ ID NO:194, SEQ ID NO:195, SEQ ID NO:196,SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199, SEQ ID NO:200, SEQ IDNO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ ID NO:204, SEQ ID NO:205, SEQID NO:206, SEQ ID NO:207, SEQ ID NO:208, SEQ ID NO:209, SEQ ID NO:210,SEQ ID NO:211, SEQ ID NO:212, SEQ ID NO:213, SEQ ID NO:214, SEQ IDNO:215, SEQ ID NO:216, SEQ ID NO:217, SEQ ID NO:218, SEQ ID NO:219, SEQID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQ ID NO:223, SEQ ID NO:224,SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227, SEQ ID NO:228, SEQ IDNO:229, SEQ ID NO:230, SEQ ID NO:231, SEQ ID NO:232, SEQ ID NO:233, SEQID NO:234, SEQ ID NO:235, SEQ ID NO:236, SEQ ID NO:237, SEQ ID NO:238,SEQ ID NO:239, SEQ ID NO:240, SEQ ID NO:241, SEQ ID NO:242, SEQ IDNO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ ID NO:246, SEQ ID NO:247, SEQID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQ ID NO:251, SEQ ID NO:252,SEQ ID NO:253, SEQ ID NO:254, SEQ ID NO:255, SEQ ID NO:256, SEQ IDNO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ ID NO:260, SEQ ID NO:261, SEQID NO:262, SEQ ID NO:263, SEQ ID NO:264, SEQ ID NO:265, SEQ ID NO:266,SEQ ID NO:267, SEQ ID NO:268, SEQ ID NO:269, SEQ ID NO:270, SEQ IDNO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ ID NO:274, SEQ ID NO:275, SEQID NO:276, SEQ ID NO:277, SEQ ID NO:278, SEQ ID NO:279, SEQ ID NO:280,SEQ ID NO:281, SEQ ID NO:282, SEQ ID NO:283, SEQ ID NO:284, SEQ IDNO:285, SEQ ID NO:286, SEQ ID NO:287, SEQ ID NO:288, SEQ ID NO:289, SEQID NO:290, SEQ ID NO:291, SEQ ID NO:292, SEQ ID NO:293, SEQ ID NO:294,SEQ ID NO:295, SEQ ID NO:296, SEQ ID NO:297, SEQ ID NO:298, SEQ IDNO:299, SEQ ID NO:300, SEQ ID NO:301, SEQ ID NO:302, SEQ ID NO:303, SEQID NO:304, SEQ ID NO:305, SEQ ID NO:306, SEQ ID NO:307, SEQ ID NO:308,SEQ ID NO:309, SEQ ID NO:310, SEQ ID NO:311, SEQ ID NO:312, SEQ IDNO:313, SEQ ID NO:314, SEQ ID NO:315, SEQ ID NO:316, SEQ ID NO:317, SEQID NO:318, SEQ ID NO:319, SEQ ID NO:320, SEQ ID NO:321, SEQ ID NO:322,SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325, SEQ ID NO:326, SEQ IDNO:327, SEQ ID NO:328, SEQ ID NO:329, SEQ ID NO:330, SEQ ID NO:331, SEQID NO:332, SEQ ID NO:2697, SEQ ID NO:2645, SEQ ID NO:2707, SEQ IDNO:2679, SEQ ID NO:2717, SEQ ID NO:2646, SEQ ID NO:2667, SEQ ID NO:2706,SEQ ID NO:2740, SEQ ID NO:2669, SEQ ID NO:2674, SEQ ID NO:2743, SEQ IDNO:2716, SEQ ID NO:2727, SEQ ID NO:2721, SEQ ID NO:2641, SEQ ID NO:2671,SEQ ID NO:2752, SEQ ID NO:2737, SEQ ID NO:2719, SEQ ID NO:2684, SEQ IDNO:2677, SEQ ID NO:2748, SEQ ID NO:2703, SEQ ID NO:2711, SEQ ID NO:2663,SEQ ID NO:2657, SEQ ID NO:2683, SEQ ID NO:2686, SEQ ID NO:2687, SEQ IDNO:2644, SEQ ID NO:2664, SEQ ID NO:2747, SEQ ID NO:2744, SEQ ID NO:2678,SEQ ID NO:2731, SEQ ID NO:2713, SEQ ID NO:2736, SEQ ID NO:2708, SEQ IDNO:2670, SEQ ID NO:2661, SEQ ID NO:2680, SEQ ID NO:2754, SEQ ID NO:2728,SEQ ID NO:2742, SEQ ID NO:2668, SEQ ID NO:2750, SEQ ID NO:2746, SEQ IDNO:2738, SEQ ID NO:2627, SEQ ID NO:2739, SEQ ID NO:2647, SEQ ID NO:2628,SEQ ID NO:2638, SEQ ID NO:2725, SEQ ID NO:2714, SEQ ID NO:2635, SEQ IDNO:2751, SEQ ID NO:2629, SEQ ID NO:2695, SEQ ID NO:2741, SEQ ID NO:2691,SEQ ID NO:2726, SEQ ID NO:2722, SEQ ID NO:2689, SEQ ID NO:2734, SEQ IDNO:2631, SEQ ID NO:2656, SEQ ID NO:2696, SEQ ID NO:2676, SEQ ID NO:2701,SEQ ID NO:2730, SEQ ID NO:2710, SEQ ID NO:2632, SEQ ID NO:2724, SEQ IDNO:2698, SEQ ID NO:2662, SEQ ID NO:2753, SEQ ID NO:2704, SEQ ID NO:2675,SEQ ID NO:2700, SEQ ID NO:2640, SEQ ID NO:2723, SEQ ID NO:2658, SEQ IDNO:2688, SEQ ID NO:2735, SEQ ID NO:2702, SEQ ID NO:2681, SEQ ID NO:2755,SEQ ID NO:2715, SEQ ID NO:2732, SEQ ID NO:2652, SEQ ID NO:2651, SEQ IDNO:2718, SEQ ID NO:2673, SEQ ID NO:2733, SEQ ID NO:2712, SEQ ID NO:2659,SEQ ID NO:2654, SEQ ID NO:2636, SEQ ID NO:2639, SEQ ID NO:2690, SEQ IDNO:2705, SEQ ID NO:2685, SEQ ID NO:2692, SEQ ID NO:2693, SEQ ID NO:2648,SEQ ID NO:2650, SEQ ID NO:2720, SEQ ID NO:2660, SEQ ID NO:2666, SEQ IDNO:2699, SEQ ID NO:2633, SEQ ID NO:2672, SEQ ID NO:2642, SEQ ID NO:2682,SEQ ID NO:2655, SEQ ID NO:2630, SEQ ID NO:2745, SEQ ID NO:2643, SEQ IDNO:2694, SEQ ID NO:2749, SEQ ID NO:2665, SEQ ID NO:2649, SEQ ID NO:2637,SEQ ID NO:2634, SEQ ID NO:2709, SEQ ID NO:2653, SEQ ID NO:2729, asubstantially purified oligonucleotide having the nucleotide sequenceselected from SEQ ID NO:333-664, and substantially purifiedoligonucleotides having at least 90% sequence identity to anoligonucleotide having the nucleotide sequence selected from SEQ IDNO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7,SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ IDNO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ IDNO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ IDNO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ IDNO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ IDNO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ IDNO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ IDNO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ IDNO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ IDNO:58, SEQ ID NO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ IDNO:63, SEQ ID NO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, SEQ IDNO:68, SEQ ID NO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ ID NO:72, SEQ IDNO:73, SEQ ID NO:74, SEQ ID NO:75, SEQ ID NO:76, SEQ ID NO:77, SEQ IDNO:78, SEQ ID NO:79, SEQ ID NO:80, SEQ ID NO:81, SEQ ID NO:82, SEQ IDNO:83, SEQ ID NO:84, SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87, SEQ IDNO:88, SEQ ID NO:89, SEQ ID NO:90, SEQ ID NO:91, SEQ ID NO:92, SEQ IDNO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ IDNO:98, SEQ ID NO:99, SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ IDNO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, SEQID NO:108, SEQ ID NO:109, SEQ ID NO:110, SEQ ID NO:111, SEQ ID NO:112,SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ IDNO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQID NO:122, SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126,SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ IDNO:131, SEQ ID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQ ID NO:135, SEQID NO:136, SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139, SEQ ID NO:140,SEQ ID NO:141, SEQ ID NO:142, SEQ ID NO:143, SEQ ID NO:144, SEQ IDNO:145, SEQ ID NO:146, SEQ ID NO:147, SEQ ID NO:148, SEQ ID NO:149, SEQID NO:150, SEQ ID NO:151, SEQ ID NO:152, SEQ ID NO:153, SEQ ID NO:154,SEQ ID NO:155, SEQ ID NO:156, SEQ ID NO:157, SEQ ID NO:158, SEQ IDNO:159, SEQ ID NO:160, SEQ ID NO:161, SEQ ID NO:162, SEQ ID NO:163, SEQID NO:164, SEQ ID NO:165, SEQ ID NO:166, SEQ ID NO:167, SEQ ID NO:168,SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171, SEQ ID NO:172, SEQ IDNO:173, SEQ ID NO:174, SEQ ID NO:175, SEQ ID NO:176, SEQ ID NO:177, SEQID NO:178, SEQ ID NO:179, SEQ ID NO:180, SEQ ID NO:181, SEQ ID NO:182,SEQ ID NO:183, SEQ ID NO:184, SEQ ID NO:185, SEQ ID NO:186, SEQ IDNO:187, SEQ ID NO:188, SEQ ID NO:189, SEQ ID NO:190, SEQ ID NO:191, SEQID NO:192, SEQ ID NO:193, SEQ ID NO:194, SEQ ID NO:195, SEQ ID NO:196,SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199, SEQ ID NO:200, SEQ IDNO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ ID NO:204, SEQ ID NO:205, SEQID NO:206, SEQ ID NO:207, SEQ ID NO:208, SEQ ID NO:209, SEQ ID NO:210,SEQ ID NO:211, SEQ ID NO:212, SEQ ID NO:213, SEQ ID NO:214, SEQ IDNO:215, SEQ ID NO:216, SEQ ID NO:217, SEQ ID NO:218, SEQ ID NO:219, SEQID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQ ID NO:223, SEQ ID NO:224,SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227, SEQ ID NO:228, SEQ IDNO:229, SEQ ID NO:230, SEQ ID NO:231, SEQ ID NO:232, SEQ ID NO:233, SEQID NO:234, SEQ ID NO:235, SEQ ID NO:236, SEQ ID NO:237, SEQ ID NO:238,SEQ ID NO:239, SEQ ID NO:240, SEQ ID NO:241, SEQ ID NO:242, SEQ IDNO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ ID NO:246, SEQ ID NO:247, SEQID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQ ID NO:251, SEQ ID NO:252,SEQ ID NO:253, SEQ ID NO:254, SEQ ID NO:255, SEQ ID NO:256, SEQ IDNO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ ID NO:260, SEQ ID NO:261, SEQID NO:262, SEQ ID NO:263, SEQ ID NO:264, SEQ ID NO:265, SEQ ID NO:266,SEQ ID NO:267, SEQ ID NO:268, SEQ ID NO:269, SEQ ID NO:270, SEQ IDNO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ ID NO:274, SEQ ID NO:275, SEQID NO:276, SEQ ID NO:277, SEQ ID NO:278, SEQ ID NO:279, SEQ ID NO:280,SEQ ID NO:281, SEQ ID NO:282, SEQ ID NO:283, SEQ ID NO:284, SEQ IDNO:285, SEQ ID NO:286, SEQ ID NO:287, SEQ ID NO:288, SEQ ID NO:289, SEQID NO:290, SEQ ID NO:291, SEQ ID NO:292, SEQ ID NO:293, SEQ ID NO:294,SEQ ID NO:295, SEQ ID NO:296, SEQ ID NO:297, SEQ ID NO:298, SEQ-IDNO:299, SEQ ID NO:300, SEQ ID NO:301, SEQ ID NO:302, SEQ ID NO:303, SEQID NO:304, SEQ ID NO:305, SEQ ID NO:306, SEQ ID NO:307, SEQ ID NO:308,SEQ ID NO:309, SEQ ID NO:310, SEQ ID NO:311, SEQ ID NO:312, SEQ IDNO:313, SEQ ID NO:314, SEQ ID NO:315, SEQ ID NO:316, SEQ ID NO:317, SEQID NO:318, SEQ ID NO:319, SEQ ID NO:320, SEQ ID NO:321, SEQ ID NO:322,SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325, SEQ ID NO:326, SEQ IDNO:327, SEQ ID NO:328, SEQ ID NO:329, SEQ ID NO:330, SEQ ID NO:331, SEQID NO:332, SEQ ID NO:2697, SEQ ID NO:2645, SEQ ID NO:2707, SEQ IDNO:2679, SEQ ID NO:2717, SEQ ID NO:2646, SEQ ID NO:2667, SEQ ID NO:2706,SEQ ID NO:2740, SEQ ID NO:2669, SEQ ID NO:2674, SEQ ID NO:2743, SEQ IDNO:2716, SEQ ID NO:2727, SEQ ID NO:2721, SEQ ID NO:2641, SEQ ID NO:2671,SEQ ID NO:2752, SEQ ID NO:2737, SEQ ID NO:2719, SEQ ID NO:2684, SEQ IDNO:2677, SEQ ID NO:2748, SEQ ID NO:2703, SEQ ID NO:2711, SEQ ID NO:2663,SEQ ID NO:2657, SEQ ID NO:2683, SEQ ID NO:2686, SEQ ID NO:2687, SEQ IDNO:2644, SEQ ID NO:2664, SEQ ID NO:2747, SEQ ID NO:2744, SEQ ID NO:2678,SEQ ID NO:2731, SEQ ID NO:2713, SEQ ID NO:2736, SEQ ID NO:2708, SEQ IDNO:2670, SEQ ID NO:2661, SEQ ID NO:2680, SEQ ID NO:2754, SEQ ID NO:2728,SEQ ID NO:2742, SEQ ID NO:2668, SEQ ID NO:2750, SEQ ID NO:2746, SEQ IDNO:2738, SEQ ID NO:2627, SEQ ID NO:2739, SEQ ID NO:2647, SEQ ID NO:2628,SEQ ID NO:2638, SEQ ID NO:2725, SEQ ID NO:2714, SEQ ID NO:2635, SEQ IDNO:2751, SEQ ID NO:2629, SEQ ID NO:2695, SEQ ID NO:2741, SEQ ID NO:2691,SEQ ID NO:2726, SEQ ID NO:2722, SEQ ID NO:2689, SEQ ID NO:2734, SEQ IDNO:2631, SEQ ID NO:2656, SEQ ID NO:2696, SEQ ID NO:2676, SEQ ID NO:2701,SEQ ID NO:2730, SEQ ID NO:2710, SEQ ID NO:2632, SEQ ID NO:2724, SEQ IDNO:2698, SEQ ID NO:2662, SEQ ID NO:2753, SEQ ID NO:2704, SEQ ID NO:2675,SEQ ID NO:2700, SEQ ID NO:2640, SEQ ID NO:2723, SEQ ID NO:2658, SEQ IDNO:2688, SEQ ID NO:2735, SEQ ID NO:2702, SEQ ID NO:2681, SEQ ID NO:2755,SEQ ID NO:2715, SEQ ID NO:2732, SEQ ID NO:2652, SEQ ID NO:2651, SEQ IDNO:2718, SEQ ID NO:2673, SEQ ID NO:2733, SEQ ID NO:2712, SEQ ID NO:2659,SEQ ID NO:2654, SEQ ID NO:2636, SEQ ID NO:2639, SEQ ID NO:2690, SEQ IDNO:2705, SEQ ID NO:2685, SEQ ID NO:2692, SEQ ID NO:2693, SEQ ID NO:2648,SEQ ID NO:2650, SEQ ID NO:2720, SEQ ID NO:2660, SEQ ID NO:2666, SEQ IDNO:2699, SEQ ID NO:2633, SEQ ID NO:2672, SEQ ID NO:2642, SEQ ID NO:2682,SEQ ID NO:2655, SEQ ID NO:2630, SEQ ID NO:2745, SEQ ID NO:2643, SEQ IDNO:2694, SEQ ID NO:2749, SEQ ID NO:2665, SEQ ID NO:2649, SEQ ID NO:2637,SEQ ID NO:2634, SEQ ID NO:2709, SEQ ID NO:2653, SEQ ID NO:2729 and/orSEQ ID NO:333-664. In a further aspect, the invention is directed to asubstantially purified oligonucleotide that hybridizes at highstringency to an oligonucleotide having the nucleotide sequence selectedfrom SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6,SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQID NO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ IDNO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ IDNO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ IDNO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ IDNO:32, SEQ ID NO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ IDNO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ IDNO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ IDNO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ IDNO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ IDNO:57, SEQ ID NO:58, SEQ ID NO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ IDNO:62, SEQ ID NO:63, SEQ ID NO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ IDNO:67, SEQ ID NO:68, SEQ ID NO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ IDNO:72, SEQ ID NO:73, SEQ ID NO:74, SEQ ID NO:75, SEQ ID NO:76, SEQ IDNO:77, SEQ ID NO:78, SEQ ID NO:79, SEQ ID NO:80, SEQ ID NO:81, SEQ IDNO:82, SEQ ID NO:83, SEQ ID NO:84, SEQ ID NO:85, SEQ ID NO:86, SEQ IDNO:87, SEQ ID NO:88, SEQ ID NO:89, SEQ ID NO:90, SEQ ID NO:91, SEQ IDNO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ IDNO:97, SEQ ID NO:98, SEQ ID NO:99, SEQ ID NO:100, SEQ ID NO:101, SEQ IDNO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQID NO:107, SEQ ID NO:108, SEQ ID NO:109, SEQ ID NO:110, SEQ ID NO:111,SEQ ID NO:112, SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ IDNO:116, SEQ ID NO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQID NO:121, SEQ ID NO:122, SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125,SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ IDNO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQID NO:135, SEQ ID NO:136, SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139,SEQ ID NO:140, SEQ ID NO:141, SEQ ID NO:142, SEQ ID NO:143, SEQ IDNO:144, SEQ ID NO:145, SEQ ID NO:146, SEQ ID NO:147, SEQ ID NO:148, SEQID NO:149, SEQ ID NO:150, SEQ ID NO:151, SEQ ID NO:152, SEQ ID NO:153,SEQ ID NO:154, SEQ ID NO:155, SEQ ID NO:156, SEQ ID NO:157, SEQ IDNO:158, SEQ ID NO:159, SEQ ID NO:160, SEQ ID NO:161, SEQ ID NO:162, SEQID NO:163, SEQ ID NO:164, SEQ ID NO:165, SEQ ID NO:166, SEQ ID NO:167,SEQ ID NO:168, SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171, SEQ IDNO:172, SEQ ID NO:173, SEQ ID NO:174, SEQ ID NO:175, SEQ ID NO:176, SEQID NO:177, SEQ ID NO:178, SEQ ID NO:179, SEQ ID NO:180, SEQ ID NO:181,SEQ ID NO:182, SEQ ID NO:183, SEQ ID NO:184, SEQ ID NO:185, SEQ IDNO:186, SEQ ID NO:187, SEQ ID NO:188, SEQ ID NO:189, SEQ ID NO:190, SEQID NO:191, SEQ ID NO:192, SEQ ID NO:193, SEQ ID NO:194, SEQ ID NO:195,SEQ ID NO:196, SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199, SEQ IDNO:200, SEQ ID NO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ ID NO:204, SEQID NO:205, SEQ ID NO:206, SEQ ID NO:207, SEQ ID NO:208, SEQ ID NO:209,SEQ ID NO:210, SEQ ID NO:211, SEQ ID NO:212, SEQ ID NO:213, SEQ IDNO:214, SEQ ID NO:215, SEQ ID NO:216, SEQ ID NO:217, SEQ ID NO:218, SEQID NO:219, SEQ ID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQ ID NO:223,SEQ ID NO:224, SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227, SEQ IDNO:228, SEQ ID NO:229, SEQ ID NO:230, SEQ ID NO:231, SEQ ID NO:232, SEQID NO:233, SEQ ID NO:234, SEQ ID NO:235, SEQ ID NO:236, SEQ ID NO:237,SEQ ID NO:238, SEQ ID NO:239, SEQ ID NO:240, SEQ ID NO:241, SEQ IDNO:242, SEQ ID NO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ ID NO:246, SEQID NO:247, SEQ ID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQ ID NO:251,SEQ ID NO:252, SEQ ID NO:253, SEQ ID NO:254, SEQ ID NO:255, SEQ IDNO:256, SEQ ID NO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ ID NO:260, SEQID NO:261, SEQ ID NO:262, SEQ ID NO:263, SEQ ID NO:264, SEQ ID NO:265,SEQ ID NO:266, SEQ ID NO:267, SEQ ID NO:268, SEQ ID NO:269, SEQ IDNO:270, SEQ ID NO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ ID NO:274, SEQID NO:275, SEQ ID NO:276, SEQ ID NO:277, SEQ ID NO:278, SEQ ID NO:279,SEQ ID NO:280, SEQ ID NO:281, SEQ ID NO:282, SEQ ID NO:283, SEQ IDNO:284, SEQ ID NO:285, SEQ ID NO:286, SEQ ID NO:287, SEQ ID NO:288, SEQID NO:289, SEQ ID NO:290, SEQ ID NO:291, SEQ ID NO:292, SEQ ID NO:293,SEQ ID NO:294, SEQ ID NO:295, SEQ ID NO:296, SEQ ID NO:297, SEQ IDNO:298, SEQ ID NO:299, SEQ ID NO:300, SEQ ID NO:301, SEQ ID NO:302, SEQID NO:303, SEQ ID NO:304, SEQ ID NO:305, SEQ ID NO:306, SEQ ID NO:307,SEQ ID NO:308, SEQ ID NO:309, SEQ ID NO:310, SEQ ID NO:311, SEQ IDNO:312, SEQ ID NO:313, SEQ ID NO:314, SEQ ID NO:315, SEQ ID NO:316, SEQID NO:317, SEQ ID NO:318, SEQ ID NO:319, SEQ ID NO:320, SEQ ID NO:321,SEQ ID NO:322, SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325, SEQ IDNO:326, SEQ ID NO:327, SEQ ID NO:328, SEQ ID NO:329, SEQ ID NO:330, SEQID NO:331, SEQ ID NO:332, SEQ ID NO:2697, SEQ ID NO:2645, SEQ IDNO:2707, SEQ ID NO:2679, SEQ ID NO:2717, SEQ ID NO:2646, SEQ ID NO:2667,SEQ ID NO:2706, SEQ ID NO:2740, SEQ ID NO:2669, SEQ ID NO:2674, SEQ IDNO:2743, SEQ ID NO:2716, SEQ ID NO:2727, SEQ ID NO:2721, SEQ ID NO:2641,SEQ ID NO:2671, SEQ ID NO:2752, SEQ ID NO:2737, SEQ ID NO:2719, SEQ IDNO:2684, SEQ ID NO:2677, SEQ ID NO:2748, SEQ ID NO:2703, SEQ ID NO:2711,SEQ ID NO:2663, SEQ ID NO:2657, SEQ ID NO:2683, SEQ ID NO:2686, SEQ IDNO:2687, SEQ ID NO:2644, SEQ ID NO:2664, SEQ ID NO:2747, SEQ ID NO:2744,SEQ ID NO:2678, SEQ ID NO:2731, SEQ ID NO:2713, SEQ ID NO:2736, SEQ IDNO:2708, SEQ ID NO:2670, SEQ ID NO:2661, SEQ ID NO:2680, SEQ ID NO:2754,SEQ ID NO:2728, SEQ ID NO:2742, SEQ ID NO:2668, SEQ ID NO:2750, SEQ IDNO:2746, SEQ ID NO:2738, SEQ ID NO:2627, SEQ ID NO:2739, SEQ ID NO:2647,SEQ ID NO:2628, SEQ ID NO:2638, SEQ ID NO:2725, SEQ ID NO:2714, SEQ IDNO:2635, SEQ ID NO:2751, SEQ ID NO:2629, SEQ ID NO:2695, SEQ ID NO:2741,SEQ ID NO:2691, SEQ ID NO:2726, SEQ ID NO:2722, SEQ ID NO:2689, SEQ IDNO:2734, SEQ ID NO:2631, SEQ ID NO:2656, SEQ ID NO:2696, SEQ ID NO:2676,SEQ ID NO:2701, SEQ ID NO:2730, SEQ ID NO:2710, SEQ ID NO:2632, SEQ IDNO:2724, SEQ ID NO:2698, SEQ ID NO:2662, SEQ ID NO:2753, SEQ ID NO:2704,SEQ ID NO:2675, SEQ ID NO:2700, SEQ ID NO:2640, SEQ ID NO:2723, SEQ IDNO:2658, SEQ ID NO:2688, SEQ ID NO:2735, SEQ ID NO:2702, SEQ ID NO:2681,SEQ ID NO:2755, SEQ ID NO:2715, SEQ ID NO:2732, SEQ ID NO:2652, SEQ IDNO:2651, SEQ ID NO:2718, SEQ ID NO:2673, SEQ ID NO:2733, SEQ ID NO:2712,SEQ ID NO:2659, SEQ ID NO:2654, SEQ ID NO:2636, SEQ ID NO:2639, SEQ IDNO:2690, SEQ ID NO:2705, SEQ ID NO:2685, SEQ ID NO:2692, SEQ ID NO:2693,SEQ ID NO:2648, SEQ ID NO:2650, SEQ ID NO:2720, SEQ ID NO:2660, SEQ IDNO:2666, SEQ ID NO:2699, SEQ ID NO:2633, SEQ ID NO:2672, SEQ ID NO:2642,SEQ ID NO:2682, SEQ ID NO:2655, SEQ ID NO:2630, SEQ ID NO:2745, SEQ IDNO:2643, SEQ ID NO:2694, SEQ ID NO:2749, SEQ ID NO:2665, SEQ ID NO:2649,SEQ ID NO:2637, SEQ ID NO:2634, SEQ ID NO:2709, SEQ ID NO:2653, SEQ IDNO:2729 or SEQ ID NOS:333-664. The sequences may be used as diagnosticoligonucleotides for transplant rejection and/or cardiac transplantrejection. The oligonucleotide may have nucleotide sequence includingDNA, cDNA, PNA, genomic DNA, or synthetic oligonucleotides.

In another aspect, the invention is directed to a method of diagnosingor monitoring transplant rejection in a patient wherein the expressionlevel of one or more genes in a patient's bodily fluid is detected. In afurther variation, the bodily fluid is peripheral blood.

In another aspect, the invention is directed to a method of diagnosingor monitoring transplant rejection in a patient, comprising detectingthe expression level of four or more genes in the patient to diagnose ormonitor transplant rejection in the patient wherein the four or moregenes include a nucleotide sequence selected from SEQ ID NO:2, SEQ IDNO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8,SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ IDNO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ IDNO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ IDNO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ IDNO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ IDNO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ IDNO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ IDNO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ IDNO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ ID NO:58, SEQ IDNO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ ID NO:63, SEQ IDNO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, SEQ ID NO:68, SEQ IDNO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ IDNO:74, SEQ ID NO:75, SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ IDNO:79, SEQ ID NO:80, SEQ ID NO:81, SEQ ID NO:82, SEQ ID NO:83, SEQ IDNO:84, SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87, SEQ ID NO:88, SEQ IDNO:89, SEQ ID NO:90, SEQ ID NO:91, SEQ ID NO:92, SEQ ID NO:93, SEQ IDNO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ ID NO:98, SEQ IDNO:99, SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, SEQ ID NO:108,SEQ ID NO:109, SEQ ID NO:110, SEQ ID NO:111, SEQ ID NO:112, SEQ IDNO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ ID NO:117, SEQID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQ ID NO:122,SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQ IDNO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQ ID NO:135, SEQ ID NO:136,SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139, SEQ ID NO:140, SEQ IDNO:141, SEQ ID NO:142, SEQ ID NO:143, SEQ ID NO:144, SEQ ID NO:145, SEQID NO:146, SEQ ID NO:147, SEQ ID NO:148, SEQ ID NO:149, SEQ ID NO:150,SEQ ID NO:151, SEQ ID NO:152, SEQ ID NO:153, SEQ ID NO:154, SEQ IDNO:155, SEQ ID NO:156, SEQ ID NO:157, SEQ ID NO:158, SEQ ID NO:159, SEQID NO:160, SEQ ID NO:161, SEQ ID NO:162, SEQ ID NO:163, SEQ ID NO:164,SEQ ID NO:165, SEQ ID NO:166, SEQ ID NO:167, SEQ ID NO:168, SEQ IDNO:169, SEQ ID NO:170, SEQ ID NO:171, SEQ ID NO:172, SEQ ID NO:173, SEQID NO:174, SEQ ID NO:175, SEQ ID NO:176, SEQ ID NO:177, SEQ ID NO:178,SEQ ID NO:179, SEQ ID NO:180, SEQ ID NO:181, SEQ ID NO:182, SEQ IDNO:183, SEQ ID NO:184, SEQ ID NO:185, SEQ ID NO:186, SEQ ID NO:187, SEQID NO:188, SEQ ID NO:189, SEQ ID NO:190, SEQ ID NO:191, SEQ ID NO:192,SEQ ID NO:193, SEQ ID NO:194, SEQ ID NO:195, SEQ ID NO:196, SEQ IDNO:197, SEQ ID NO:198, SEQ ID NO:199, SEQ ID NO:200, SEQ ID NO:201, SEQID NO:202, SEQ ID NO:203, SEQ ID NO:204, SEQ ID NO:205, SEQ ID NO:206,SEQ ID NO:207, SEQ ID NO:208, SEQ ID NO:209, SEQ ID NO:210, SEQ IDNO:211, SEQ ID NO:212, SEQ ID NO:213, SEQ ID NO:214, SEQ ID NO:215, SEQID NO:216, SEQ ID NO:217, SEQ ID NO:218, SEQ ID NO:219, SEQ ID NO:220,SEQ ID NO:221, SEQ ID NO:222, SEQ ID NO:223, SEQ ID NO:224, SEQ IDNO:225, SEQ ID NO:226, SEQ ID NO:227, SEQ ID NO:228, SEQ ID NO:229, SEQID NO:230, SEQ ID NO:231, SEQ ID NO:232, SEQ ID NO:233, SEQ ID NO:234,SEQ ID NO:235, SEQ ID NO:236, SEQ ID NO:237, SEQ ID NO:238, SEQ IDNO:239, SEQ ID NO:240, SEQ ID NO:241, SEQ ID NO:242, SEQ ID NO:243, SEQID NO:244, SEQ ID NO:245, SEQ ID NO:246, SEQ ID NO:247, SEQ ID NO:248,SEQ ID NO:249, SEQ ID NO:250, SEQ ID NO:251, SEQ ID NO:252, SEQ IDNO:253, SEQ ID NO:254, SEQ ID NO:255, SEQ ID NO:256, SEQ ID NO:257, SEQID NO:258, SEQ ID NO:259, SEQ ID NO:260, SEQ ID NO:261, SEQ ID NO:262,SEQ ID NO:263, SEQ ID NO:264, SEQ ID NO:265, SEQ ID NO:266, SEQ IDNO:267, SEQ ID NO:268, SEQ ID NO:269, SEQ ID NO:270, SEQ ID NO:271, SEQID NO:272, SEQ ID NO:273, SEQ ID NO:274, SEQ ID NO:275, SEQ ID NO:276,SEQ ID NO:277, SEQ ID NO:278, SEQ ID NO:279, SEQ ID NO:280, SEQ IDNO:281, SEQ ID NO:282, SEQ ID NO:283, SEQ ID NO:284, SEQ ID NO:285, SEQID NO:286, SEQ ID NO:287, SEQ ID NO:288, SEQ ID NO:289, SEQ ID NO:290,SEQ ID NO:291, SEQ ID NO:292, SEQ ID NO:293, SEQ ID NO:294, SEQ IDNO:295, SEQ ID NO:296, SEQ ID NO:297, SEQ ID NO:298, SEQ ID NO:299, SEQID NO:300, SEQ ID NO:301, SEQ ID NO:302, SEQ ID NO:303, SEQ ID NO:304,SEQ ID NO:305, SEQ ID NO:306, SEQ ID NO:307, SEQ ID NO:308, SEQ IDNO:309, SEQ ID NO:310, SEQ ID NO:311, SEQ ID NO:312, SEQ ID NO:313, SEQID NO:314, SEQ ID NO:315, SEQ ID NO:316, SEQ ID NO:317, SEQ ID NO:318,SEQ ID NO:319, SEQ ID NO:320, SEQ ID NO:321, SEQ ID NO:322, SEQ IDNO:323, SEQ ID NO:324, SEQ ID NO:325, SEQ ID NO:326; SEQ ID NO:327, SEQID NO:328, SEQ ID NO:329, SEQ ID NO:330, SEQ ID NO:331, SEQ ID NO:332,SEQ ID NO:2697, SEQ ID NO:2645, SEQ ID NO:2707, SEQ ID NO:2679, SEQ IDNO:2717, SEQ ID NO:2646, SEQ ID NO:2667, SEQ ID NO:2706, SEQ ID NO:2749,SEQ ID NO:2669, SEQ ID NO:2674, SEQ ID NO:2743, SEQ ID NO:2716, SEQ IDNO:2727, SEQ ID NO:2721, SEQ ID NO:2641, SEQ ID NO:2671, SEQ ID NO:2752,SEQ ID NO:2737, SEQ ID NO:2719, SEQ ID NO:2684, SEQ ID NO:2677, SEQ IDNO:2748, SEQ ID NO:2703, SEQ ID NO:2711, SEQ ID NO:2663, SEQ ID NO:2657,SEQ ID NO:2683, SEQ ID NO:2686, SEQ ID NO:2687, SEQ ID NO:2644, SEQ IDNO:2664, SEQ ID NO:2747, SEQ ID NO:2744, SEQ ID NO:2678, SEQ ID NO:2731,SEQ ID NO:2713, SEQ ID NO:2736, SEQ ID NO:2708, SEQ ID NO:2670, SEQ IDNO:2661, SEQ ID NO:2680, SEQ ID NO:2754, SEQ ID NO:2728, SEQ ID NO:2742,SEQ ID NO:2668, SEQ ID NO:2750, SEQ ID NO:2746, SEQ ID NO:2738, SEQ IDNO:2627, SEQ ID NO:2739, SEQ ID NO:2647, SEQ ID NO:2628, SEQ ID NO:2638,SEQ ID NO:2725, SEQ ID NO:2714, SEQ ID NO:2635, SEQ ID NO:2751, SEQ IDNO:2629, SEQ ID NO:2695, SEQ ID NO:2741, SEQ ID NO:2691, SEQ ID NO:2726,SEQ ID NO:2722, SEQ ID NO:2689, SEQ ID NO:2734, SEQ ID NO:2631, SEQ IDNO:2656, SEQ ID NO:2696, SEQ ID NO:2676, SEQ ID NO:2701, SEQ ID NO:2730,SEQ ID NO:2710, SEQ ID NO:2632, SEQ ID NO:2724, SEQ ID NO:2698, SEQ IDNO:2662, SEQ ID NO:2753, SEQ ID NO:2704, SEQ ID NO:2675, SEQ ID NO:2700,SEQ ID NO:2640, SEQ ID NO:2723, SEQ ID NO:2658, SEQ ID NO:2688, SEQ IDNO:2735, SEQ ID NO:2702, SEQ ID NO:2681, SEQ ID NO:2755, SEQ ID NO:2715,SEQ ID NO:2732, SEQ ID NO:2652, SEQ ID NO:2651, SEQ ID NO:2718, SEQ IDNO:2673, SEQ ID NO:2733, SEQ ID NO:2712, SEQ ID NO:2659, SEQ ID NO:2654,SEQ ID NO:2636, SEQ ID NO:2639, SEQ ID NO:2690, SEQ ID NO:2705, SEQ IDNO:2685, SEQ ID NO:2692, SEQ ID NO:2693, SEQ ID NO:2648, SEQ ID NO:2650,SEQ ID NO:2720, SEQ ID NO:2660, SEQ ID NO:2666, SEQ ID NO:2699, SEQ IDNO:2633, SEQ ID NO:2672, SEQ ID NO:2642, SEQ ID NO:2682, SEQ ID NO:2655,SEQ ID NO:2630, SEQ ID NO:2745, SEQ ID NO:2643, SEQ ID NO:2694, SEQ IDNO:2749, SEQ ID NO:2665, SEQ ID NO:2649, SEQ ID NO:2637, SEQ ID NO:2634,SEQ ID NO:2709, SEQ ID NO:2653, SEQ ID NO:2729.

In another aspect, the invention is directed to a method of diagnosingor monitoring kidney transplant rejection in a patient by detecting oneor more proteins in a bodily fluid of the patient to diagnose or monitortransplant rejection in the patient wherein the one or more proteinshave a protein sequence selected from SEQ ID NO:76, SEQ ID NO:2663, SEQID NO:98, SEQ ID NO:2696, SEQ ID NO:2736, SEQ ID NO:2751, SEQ IDNO:2631, SEQ ID NO:2675, SEQ ID NO:2700, and SEQ ID NO:2693.

In a further aspect, the invention is also directed to a system fordetecting gene expression in body fluid including at least two isolatedpolynucleotides wherein the isolated polynucleotides detect expressionof a gene wherein the gene includes a nucleotide sequence selected fromSEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ IDNO:7, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ IDNO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ IDNO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ IDNO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ IDNO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ IDNO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ IDNO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ IDNO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ IDNO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ IDNO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ IDNO:54, SEQ ID NO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ IDNO:63, SEQ ID NO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ ID NO:67, SEQ IDNO:68, SEQ ID NO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ ID NO:72, SEQ IDNO:73, SEQ ID NO:74, SEQ ID NO:82, SEQ ID NO:83, SEQ ID NO:84, SEQ IDNO:85, SEQ ID NO:86, SEQ ID NO:87, SEQ ID NO:88, SEQ ID NO:90, SEQ IDNO:91, SEQ ID NO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ IDNO:96, SEQ ID NO:98, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ IDNO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, SEQ ID NO:108, SEQID NO:109, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ ID NO:117,SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQ IDNO:122, SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131,SEQ ID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQ ID NO:135, SEQ IDNO:136, SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139, SEQ ID NO:152, SEQID NO:153, SEQ ID NO:154, SEQ ID NO:155, SEQ ID NO:156, SEQ ID NO:157,SEQ ID NO:158, SEQ ID NO:159, SEQ ID NO:160, SEQ ID NO:161, SEQ IDNO:162, SEQ ID NO:163, SEQ ID NO:164, SEQ ID NO:165, SEQ ID NO:166, SEQID NO:167, SEQ ID NO:168, SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171,SEQ ID NO:172, SEQ ID NO:173, SEQ ID NO:174, SEQ ID NO:175, SEQ IDNO:176, SEQ ID NO:177, SEQ ID NO:178, SEQ ID NO:179, SEQ ID NO:180, SEQID NO:181, SEQ ID NO:182, SEQ ID NO:183, SEQ ID NO:184, SEQ ID NO:185,SEQ ID NO:186, SEQ ID NO:187, SEQ ID NO:188, SEQ ID NO:189, SEQ IDNO:190, SEQ ID NO:191, SEQ ID NO:192, SEQ ID NO:193, SEQ ID NO:194, SEQID NO:195, SEQ ID NO:196, SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199,SEQ ID NO:200, SEQ ID NO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ IDNO:204, SEQ ID NO:205, SEQ ID NO:206, SEQ ID NO:207, SEQ ID NO:208, SEQID NO:209, SEQ ID NO:210, SEQ ID NO:211, SEQ ID NO:212, SEQ ID NO:213,SEQ ID NO:214, SEQ ID NO:215, SEQ ID NO:216, SEQ ID NO:217, SEQ IDNO:218, SEQ ID NO:219, SEQ ID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQID NO:223, SEQ ID NO:224, SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227,SEQ ID NO:228, SEQ ID NO:229, SEQ ID NO:230, SEQ ID NO:231, SEQ IDNO:232, SEQ ID NO:233, SEQ ID NO:234, SEQ ID NO:235, SEQ ID NO:236, SEQID NO:237, SEQ ID NO:238, SEQ ID NO:239, SEQ ID NO:240, SEQ ID NO:234,SEQ ID NO:242, SEQ ID NO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ IDNO:246, SEQ ID NO:247, SEQ ID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQID NO:251, SEQ ID NO:252, SEQ ID NO:253, SEQ ID NO:254, SEQ ID NO:255,SEQ ID NO:256, SEQ ID NO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ IDNO:260, SEQ ID NO:261, SEQ ID NO:262, SEQ ID NO:263, SEQ ID NO:264, SEQID NO:265, SEQ ID NO:266, SEQ ID NO:267, SEQ ID NO:268, SEQ ID NO:269,SEQ ID NO:270, SEQ ID NO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ IDNO:274, SEQ ID NO:275, SEQ ID NO:276, SEQ ID NO:277, SEQ ID NO:278, SEQID NO:279, SEQ ID NO:280, SEQ ID NO:281, SEQ ID NO:282, SEQ ID NO:283,SEQ ID NO:284, SEQ ID NO:285, SEQ ID NO:286, SEQ ID NO:287, SEQ IDNO:288, SEQ ID NO:289, SEQ ID NO:290, SEQ ID NO:291, SEQ ID NO:292, SEQID NO:293, SEQ ID NO:294, SEQ ID NO:295, SEQ ID NO:296, SEQ ID NO:297,SEQ ID NO:298, SEQ ID NO:299, SEQ ID NO:300, SEQ ID NO:301, SEQ IDNO:302, SEQ ID NO:303, SEQ ID NO:304, SEQ ID NO:305, SEQ ID NO:306, SEQID NO:307, SEQ ID NO:308, SEQ ID NO:309, SEQ ID NO:310, SEQ ID NO:311,SEQ ID NO:312, SEQ ID NO:313, SEQ ID NO:314, SEQ ID NO:315, SEQ IDNO:316, SEQ ID NO:317, SEQ ID NO:318, SEQ ID NO:319, SEQ ID NO:320, SEQID NO:321, SEQ ID NO:322, SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325,SEQ ID NO:326, SEQ ID NO:327, SEQ ID NO:328, SEQ ID NO:329, SEQ IDNO:330, SEQ ID NO:331, SEQ ID NO:332, SEQ ID NO:2697, SEQ ID NO:2645,SEQ ID NO:2707, SEQ ID NO:2679, SEQ ID NO:2717, SEQ ID NO:2646, SEQ IDNO:2667, SEQ ID NO:2706, SEQ ID NO:2740, SEQ ID NO:2669, SEQ ID NO:2674,SEQ ID NO:2743, SEQ ID NO:2716, SEQ ID NO:2727, SEQ ID NO:2721, SEQ IDNO:2641, SEQ ID NO:2671, SEQ ID NO:2752, SEQ ID NO:2737, SEQ ID NO:2719,SEQ ID NO:2684, SEQ ID NO:2677, SEQ ID NO:2748, SEQ ID NO:2703, SEQ IDNO:2711, SEQ ID NO:2663, SEQ ID NO:2657, SEQ ID NO:2683, SEQ ID NO:2686,SEQ ID NO:2687, SEQ ID NO:2644, SEQ ID NO:2664, SEQ ID NO:2747, SEQ IDNO:2744, SEQ ID NO:2678, SEQ ID NO:2731, SEQ ID NO:2713, SEQ ID NO:2736,SEQ ID NO:2708, SEQ ID NO:2670, SEQ ID NO:2661, SEQ ID NO:2680, SEQ IDNO:2754, SEQ ID NO:2728, SEQ ID NO:2742, SEQ ID NO:2668, SEQ ID NO:2750,SEQ ID NO:2746, SEQ ID NO:2738, SEQ ID NO:2627, SEQ ID NO:2739, SEQ IDNO:2647, SEQ ID NO:2628, SEQ ID NO:2638, SEQ ID NO:2725, SEQ ID NO:2714,SEQ ID NO:2635, SEQ ID NO:2751, SEQ ID NO:2629, SEQ ID NO:2695, SEQ IDNO:2741, SEQ ID NO:2691, SEQ ID NO:2726, SEQ ID NO:2722, SEQ ID NO:2689,SEQ ID NO:2734, SEQ ID NO:2631, SEQ ID NO:2656, SEQ ID NO:2696, SEQ IDNO:2676, SEQ ID NO:2701, SEQ ID NO:2730, SEQ ID NO:2710, SEQ ID NO:2632,SEQ ID NO:2724, SEQ ID NO:2698, SEQ ID NO:2662, SEQ ID NO:2753, SEQ IDNO:2704, SEQ ID NO:2675, SEQ ID NO:2700, SEQ ID NO:2640, SEQ ID NO:2723,SEQ ID NO:2658, SEQ ID NO:2688, SEQ ID NO:2735, SEQ ID NO:2702, SEQ IDNO:2681, SEQ ID NO:2755, SEQ ID NO:2715, SEQ ID NO:2732, SEQ ID NO:2652,SEQ ID NO:2651, SEQ ID NO:2718, SEQ ID NO:2673, SEQ ID NO:2733, SEQ IDNO:2712, SEQ ID NO:2659, SEQ ID NO:2654, SEQ ID NO:2636, SEQ ID NO:2639,SEQ ID NO:2690, SEQ ID NO:2705, SEQ ID NO:2685, SEQ ID NO:2692, SEQ IDNO:2693, SEQ ID NO:2648, SEQ ID NO:2650, SEQ ID NO:2720, SEQ ID NO:2660,SEQ ID NO:2666, SEQ ID NO:2699, SEQ ID NO:2633, SEQ ID NO:2672, SEQ IDNO:2642, SEQ ID NO:2682, SEQ ID NO:2655, SEQ ID NO:2630, SEQ ID NO:2745,SEQ ID NO:2643, SEQ ID NO:2694, SEQ ID NO:2749, SEQ ID NO:2665, SEQ IDNO:2649, SEQ ID NO:2637, SEQ ID NO:2634, SEQ ID NO:2709, SEQ ID NO:2653,SEQ ID NO:2729 and the gene is differentially expressed in body fluid inan individual rejecting a transplanted organ compared to the expressionof the gene in leukocytes in an individual not rejecting a transplantedorgan.

In another aspect, the invention is directed to a system for detectinggene expression in body fluid including at least two isolatedpolynucleotides wherein the isolated polynucleotides detect expressionof a gene wherein the gene includes a nucleotide sequence selected fromSEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ IDNO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ IDNO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ IDNO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ IDNO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ IDNO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ IDNO:32, SEQ ID NO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ IDNO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ IDNO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ IDNO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ IDNO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ IDNO:57, SEQ ID NO:58, SEQ ID NO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ IDNO:62, SEQ ID NO:63, SEQ ID NO:64, SEQ ID NO:65, SEQ ID NO:66, SEQ IDNO:67, SEQ ID NO:68, SEQ ID NO:69, SEQ ID NO:70, SEQ ID NO:71, SEQ IDNO:72, SEQ ID NO:73, SEQ ID NO:74, SEQ ID NO:75, SEQ ID NO:76, SEQ IDNO:77, SEQ ID NO:78, SEQ ID NO:79, SEQ ID NO:80, SEQ ID NO:81, SEQ IDNO:82, SEQ ID NO:83, SEQ ID NO:84, SEQ ID NO:85, SEQ ID NO:86, SEQ IDNO:87, SEQ ID NO:88, SEQ ID NO:89, SEQ ID NO:90, SEQ ID NO:91, SEQ IDNO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ IDNO:97, SEQ ID NO:98, SEQ ID NO:99, SEQ ID NO:100, SEQ ID NO:101, SEQ IDNO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQID NO:107, SEQ ID NO:108, SEQ ID NO:109, SEQ ID NO:110, SEQ ID NO:111,SEQ ID NO:112, SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ IDNO:116, SEQ ID NO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQID NO:121, SEQ ID NO:122, SEQ ID NO:123, SEQ ID NO:124, SEQ ID NO:125,SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ IDNO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133, SEQ ID NO:134, SEQID NO:135, SEQ ID NO:136, SEQ ID NO:137, SEQ ID NO:138, SEQ ID NO:139,SEQ ID NO:140, SEQ ID NO:141, SEQ ID NO:142, SEQ ID NO:143, SEQ IDNO:144, SEQ ID NO:145, SEQ ID NO:146, SEQ ID NO:147, SEQ ID NO:148, SEQID NO:149, SEQ ID NO:150, SEQ ID NO:151, SEQ ID NO:152, SEQ ID NO:153,SEQ ID NO:154, SEQ ID NO:155, SEQ ID NO:156, SEQ ID NO:157, SEQ IDNO:158, SEQ ID NO:159, SEQ ID NO:160, SEQ ID NO:161, SEQ ID NO:162, SEQID NO:163, SEQ ID NO:164, SEQ ID NO:165, SEQ ID NO:166, SEQ ID NO:167,SEQ ID NO:168, SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171, SEQ IDNO:172, SEQ ID NO:173, SEQ ID NO:174, SEQ ID NO:175, SEQ ID NO:176, SEQID NO:177, SEQ ID NO:178, SEQ ID NO:179, SEQ ID NO:180, SEQ ID NO:181,SEQ ID NO:182, SEQ ID NO:183, SEQ ID NO:184, SEQ ID NO:185, SEQ IDNO:186, SEQ ID NO:187, SEQ ID NO:188, SEQ ID NO:189, SEQ ID NO:190, SEQID NO:191, SEQ ID NO:192, SEQ ID NO:193, SEQ ID NO:194, SEQ ID NO:195,SEQ ID NO:196, SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199, SEQ IDNO:200, SEQ ID NO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ ID NO:204, SEQID NO:205, SEQ ID NO:206, SEQ ID NO:207, SEQ ID NO:208, SEQ ID NO:209,SEQ ID NO:210, SEQ ID NO:211, SEQ ID NO:212, SEQ ID NO:213, SEQ IDNO:214, SEQ ID NO:215, SEQ ID NO:216, SEQ ID NO:217, SEQ ID NO:218, SEQID NO:219, SEQ ID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQ ID NO:223,SEQ ID NO:224, SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227, SEQ IDNO:228, SEQ ID NO:229, SEQ ID NO:230, SEQ ID NO:231, SEQ ID NO:232, SEQID NO:233, SEQ ID NO:234, SEQ ID NO:235, SEQ ID NO:236, SEQ ID NO:237,SEQ ID NO:238, SEQ ID NO:239, SEQ ID NO:240, SEQ ID NO:241, SEQ IDNO:242, SEQ ID NO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ ID NO:246, SEQID NO:247, SEQ ID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQ ID NO:251,SEQ ID NO:252, SEQ ID NO:253, SEQ ID NO:254, SEQ ID NO:255, SEQ IDNO:256, SEQ ID NO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ ID NO:260, SEQID NO:261, SEQ ID NO:262, SEQ ID NO:263, SEQ ID NO:264, SEQ ID NO:265,SEQ ID NO:266, SEQ ID NO:267, SEQ ID NO:268, SEQ ID NO:269, SEQ IDNO:270, SEQ ID NO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ ID NO:274, SEQID NO:275, SEQ ID NO:276, SEQ ID NO:277, SEQ ID NO:278, SEQ ID NO:279,SEQ ID NO:280, SEQ ID NO:281, SEQ ID NO:282, SEQ ID NO:283, SEQ IDNO:284, SEQ ID NO:285, SEQ ID NO:286, SEQ ID NO:287, SEQ ID NO:288, SEQID NO:289, SEQ ID NO:290, SEQ ID NO:291, SEQ ID NO:292, SEQ ID NO:293,SEQ ID NO:294, SEQ ID NO:295, SEQ ID NO:296, SEQ ID NO:297, SEQ IDNO:298, SEQ ID NO:299, SEQ ID NO:300, SEQ ID NO:301, SEQ ID NO:302, SEQID NO:303, SEQ ID NO:304, SEQ ID NO:305, SEQ ID NO:306, SEQ ID NO:307,SEQ ID NO:308, SEQ ID NO:309, SEQ ID NO:310, SEQ ID NO:311, SEQ IDNO:312, SEQ ID NO:313, SEQ ID NO:314, SEQ ID NO:315, SEQ ID NO:316, SEQID NO:317, SEQ ID NO:318, SEQ ID NO:319, SEQ ID NO:320, SEQ ID NO:321,SEQ ID NO:322, SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325, SEQ IDNO:326, SEQ ID NO:327, SEQ ID NO:328, SEQ ID NO:329, SEQ ID NO:330, SEQID NO:331, SEQ ID NO:332, SEQ ID NO:2697, SEQ ID NO:2645, SEQ IDNO:2707, SEQ ID NO:2679, SEQ ID NO:2717, SEQ ID NO:2646, SEQ ID NO:2667,SEQ ID NO:2706, SEQ ID NO:2740, SEQ ID NO:2669, SEQ ID NO:2674, SEQ IDNO:2743, SEQ ID NO:2716, SEQ ID NO:2727, SEQ ID NO:2721, SEQ ID NO:2641,SEQ ID NO:2671, SEQ ID NO:2752, SEQ ID NO:2737, SEQ ID NO:2719, SEQ IDNO:2684, SEQ ID NO:2677, SEQ ID NO:2748, SEQ ID NO:2703, SEQ ID NO:2711,SEQ ID NO:2663, SEQ ID NO:2657, SEQ ID NO:2683, SEQ ID NO:2686, SEQ IDNO:2687, SEQ ID NO:2644, SEQ ID NO:2664, SEQ ID NO:2747, SEQ ID NO:2744,SEQ ID NO:2678, SEQ ID NO:2731, SEQ ID NO:2713, SEQ ID NO:2736, SEQ IDNO:2708, SEQ ID NO:2670, SEQ ID NO:2661, SEQ ID NO:2680, SEQ ID NO:2754,SEQ ID NO:2728, SEQ ID NO:2742, SEQ ID NO:2668, SEQ ID NO:2750, SEQ IDNO:2746, SEQ ID NO:2738, SEQ ID NO:2627, SEQ ID NO:2739, SEQ ID NO:2647,SEQ ID NO:2628, SEQ ID NO:2638, SEQ ID NO:2725, SEQ ID NO:2714, SEQ IDNO:2635, SEQ ID NO:2751, SEQ ID NO:2629, SEQ ID NO:2695, SEQ ID NO:2741,SEQ ID NO:2691, SEQ ID NO:2726, SEQ ID NO:2722, SEQ ID NO:2689, SEQ IDNO:2734, SEQ ID NO:2631, SEQ ID NO:2656, SEQ ID NO:2696, SEQ ID NO:2676,SEQ ID NO:2701, SEQ ID NO:2730, SEQ ID NO:2710, SEQ ID NO:2632, SEQ IDNO:2724, SEQ ID NO:2698, SEQ ID NO:2662, SEQ ID NO:2753, SEQ ID NO:2704,SEQ ID NO:2675, SEQ ID NO:2700, SEQ ID NO:2640, SEQ ID NO:2723, SEQ IDNO:2658, SEQ ID NO:2688, SEQ ID NO:2735, SEQ ID NO:2702, SEQ ID NO:2681,SEQ ID NO:2755, SEQ ID NO:2715, SEQ ID NO:2732, SEQ ID NO:2652, SEQ IDNO:2651, SEQ ID NO:2718, SEQ ID NO:2673, SEQ ID NO:2733, SEQ ID NO:2712,SEQ ID NO:2659, SEQ ID NO:2654, SEQ ID NO:2636, SEQ ID NO:2639, SEQ IDNO:2690, SEQ ID NO:2705, SEQ ID NO:2685, SEQ ID NO:2692, SEQ ID NO:2693,SEQ ID NO:2648, SEQ ID NO:2650, SEQ ID NO:2720, SEQ ID NO:2660, SEQ IDNO:2666, SEQ ID NO:2699, SEQ ID NO:2633, SEQ ID NO:2672, SEQ ID NO:2642,SEQ ID NO:2682, SEQ ID NO:2655, SEQ ID NO:2630, SEQ ID NO:2745, SEQ IDNO:2643, SEQ ID NO:2694, SEQ ID NO:2746, SEQ ID NO:2665, SEQ ID NO:2649,SEQ ID NO:2637, SEQ ID NO:2634, SEQ ID NO:2709, SEQ ID NO:2653, SEQ IDNO:2729 and the gene expression is related to the rate of hematopoiesisor the distribution of hematopoeitic cells along their maturationpathway.

The invention is also directed to methods of diagnosing or monitoringtransplant rejection in a patient by detecting the expression level ofone or more genes including a nucleotide sequence selected from SEQ IDNOS: 3016-3117. SEQ ID NOS:3108-3117 are useful in detecting CMVinfection.

BRIEF DESCRIPTION OF THE SEQUENCE LISTING

SEQ ID's 1-332 are 50mer oligonucleotides corresponding to geneexpression markers for diagnosis and monitoring of allograft rejectionand other disorders.

SEQ ID's 333-664 are Reference mRNA sequences for genes identified byprobes 1-332.

SEQ ID's 665-995 are a first set of Left PCR primers for genes 1-332.

SEQ ID's 996-1326 are a first set of Right PCR primers for genes 1-332.

SEQ ID's 1327-1657 are Taqman probes for the first set PCR primers forgenes 1-332.

SEQ ID's 1658-1903 are a second alternative set of left PCR primers forselected genes 1-332

SEQ ID's 1904-2151 are a second alternative set of right PCR primers forselected genes 1-332

SEQ ID's 2152-2399 are Taqman probes for the second alternative set ofPCR primers for selected genes 1-332.

SEQ ID's 2400-2626 are Proteins encoded by mRNA's from genes identifiedin 1-332.

SEQ ID's 2627-2795 are 50mer oligonucleotide array probes used toidentify genes in FIG. 7 and Tables 6 and 8.

SEQ ID's 2796-2924 are reference mRNA sequences for genes in Table 8which show altered expression in renal transplantation and rejection.

SEQ ID's 2925-3015 are proteins coded by genes which show alteredexpression in Table 8.

SEQ ID's 3016-3081 are 50mer oligonucleotide array probes and used toidentify genes in the Examples.

SEQ ID's 3082-3107 are genes and primers discussed in the Examples.

SEQ ID's 3108-3117 are mRNAs from human genes in which regulation isaltered upon CMV infection.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: FIG. 1 is a schematic flow chart illustrating a schematicinstruction set for characterization of the nucleotide sequence and/orthe predicted protein sequence of novel nucleotide sequences.

FIG. 2: FIG. 2 depicts the components of an automated RNA preparationmachine. A primary component of the device is a centrifuge. (A.) Tubesof whole blood containing a density gradient solution,transcription/translation inhibitors, and a gel barrier that separateserythrocytes from mononuclear cells and serum after centrifugation areplaced in the centrifuge. (B.) The barrier is permeable to erythrocytesand granulocytes during centrifugation, but does not allow mononuclearcells to pass through (or the barrier substance has a density such thatmononuclear cells remain above the level of the barrier during thecentrifugation). After centrifugation, the erythrocytes and granulocytesare trapped beneath the barrier, facilitating isolation of themononuclear cell and serum layers. A mechanical arm removes the tube andinverts it to mix the mononuclear cell layer and the serum. (C.) The armnext pours the supernatant into a fresh tube (D.), while theerythrocytes and granulocytes remained below the barrier. Alternatively,a needle is used to aspirate the supernatant and transfer it to a freshtube. The mechanical arms of the device opens and closes lids, dispensesPBS to aid in the collection of the mononuclear cells by centrifugation,and moves the tubes in and out of the centrifuge. Followingcentrifugation, the supernatant is poured off or removed by a vacuumdevice (E), leaving an isolated mononuclear cell pellet. Purification ofthe RNA from the cells is performed automatically, with lysis buffer andother purification solutions (F.) automatically dispensed and removedbefore and after centrifugation steps. The result is a purified RNAsolution.

FIG. 3 shows the results of six hybridizations on a mini array graphed(n=6 for each column). The error bars are the SEM. This experiment showsthat the average signal from AP prepared RNA is 47% of the averagesignal from GS prepared RNA for both Cy3 and Cy5.

FIG. 4 shows the average background subtracted signal for each of nineleukocyte-specific genes on a mini array. This average is for 3-6 of theabove-described hybridizations for each gene. The error bars are theSEM.

FIG. 5 shows the ratio of Cy3 to Cy5 signal for a number of genes. Afternormalization, this ratio corrects for variability among hybridizationsand allows comparison between experiments done at different times. Theratio is calculated as the Cy3 background subtracted signal divided bythe Cy5 background subtracted signal. Each bar is the average for 3-6hybridizations. The error bars are SEM.

FIG. 6 shows data median Cy3 background subtracted signals for controlRNAs using mini arrays.

FIG. 7: Cardiac Allograft rejection diagnostic genes.

A. Example of rejection and no-rejection samples expression data for 5marker genes. For each sample, the associated rejection grades are shownas are the expression ratios for 5 differentially expressed genes. Thegenes are identified by the SEQ ID number for the oligonucleotide. Theaverage fold difference between grade 0 and grade 3A samples iscalculated at the bottom.

B. CART classification model. Decision tree for a 3 gene classificationmodel for diagnosis of cardiac rejection. In the first step, expressionof gene 223 is used to divide the patients to 2 branches. The remainingsamples in each branch are then further divided by one remaining gene.The samples are classified as either rejection or no rejection. 1 norejection sample is misclassified as a rejection sample.

C. Surrogates for the CART classification model. For each of the 3splitter genes in the CART rejection model described in the example, 5top surrogate genes are listed that were identified by the CARTalgorithm.

FIG. 8: Validation of differential expression of a gene discovered usingmicroarrays using real-time PCR

FIG. 8A. The Ct for each patient sample on multiple assays is shownalong with the Ct in the R50 control RNA. Triangles represent -RT(reverse transcriptase) controls.

FIG. 8B. The fold difference between the expression of Granzyme B and anActin reference is shown for 3 samples from patients with and withoutCMV disease.

FIG. 9: Endpoint testing of PCR primers

Electrophoresis and microfluidics are used to assess the product of genespecific PCR primers. A. β-GUS gel image. Lane 3 is the image forprimers F178 and R242. Lanes 2 and 1 correspond to the no-templatecontrol and -RT control, respectively.

B. The electropherogram of β-GUS primers F178 and R242, a graphicalrepresentation of Lane 3 from the gel image.

C. β-Actin gel image. Lane 3 is the image for primers F75 and R178.Lanes 2 and 1 correspond to the no-template control and -RT control,respectively.

D. The electropherogram of β-Actin primers F75 and R178, a graphicalrepresentation of Lane 3 from the gel image.

FIG. 10: PCR Primer efficiency testing. A standard curve of Ct versuslog of the starting RNA amount is shown for 2 genes.

FIG. 11: Real-time PCR control gene analysis

11 candidate control genes were tested using real-time PCR on 6 wholeblood samples (PAX) paired with 6 mononuclear samples (CPT) from thesame patient. Each sample was tested twice. For each gene, thevariability of the gene across the samples is shown on the vertical axis(A). The average C_(T) value for each gene is also shown (B). 2 μg RNAwas used for PAX samples and 0.5 μg total RNA was used for themononuclear samples (CPT).

FIG. 12: Rejection marker discovery by co-expression with establishedmarker

Microarrays were used to measure expression of genes SEQ ID 85 and 302in samples derived from 240 transplant recipients. For each sample, theexpression measurement for 85 is plotted against 302.

FIG. 13: ROC (receiver operator characteristics) curve for a 3-gene PCRassay for diagnosis of rejection (see example 17). The Sensitivity andFalse Positive Rate for each test cutoff is shown.

BRIEF DESCRIPTION OF THE TABLES

Table 1: Table 1 lists diseases or conditions amenable to study byleukocyte profiling.

Table 2: Transplant Markers

A. Transplant Genes: Genes useful for monitoring of allograft rejectionare listed in this here. The gene symbol and name are given. SEQ ID50mer is the sequence ID of a 50mer oligonucleotide that is specific forthe gene. The NCBI Unigene number (HS) from (Build 160, 16 Feb. 2003) isgiven as is an accession number (ACC) from (Genbank Release 135, 15 Apr.2003) for an RNA or cDNA is Genbank that corresponds to the gene. Thesequence identified by the ACC number is in the sequence listing (SEQ IDRNA/cDNA).

B. Microarray Data: SEQ ID 50mer, Gene, Gene Name, ACC and SEQ IDRNA/cDNA are given for each gene as in A (above). Each identified genehas a Non-Parametric Score and Median Rank in NR given from thenon-parametric analysis of the data. The genes are ranked from highestto lowest scoring. Down Regulated genes are noted with a 1 in thiscolumn.

C. PCR Primers: Primers and probes for real-time PCR assays for eachgene are given along with their SEQ ID #s. Each gene has 1 or 2 sets ofa forward and reverse PCR primer and a hybridization probe for detectionin TaqMan or similar assays.

D. PCR Data: Real-time PCR data was generated on a set of transplantsamples using sybr green technology as described in the text. For eachgene the number of samples (n) used in the analysis is given. An oddsratio and the p-values for a Fisher test and t-test are given for thecomparison of acute rejection samples is given (see text).

E. Transplant proteins: For each gene, the corresponding protein in theRefSeq data base (Genbank Release 135, 18 Apr. 2003) is given (RefSeqPeptide Accession #) along the SEQ ID for that protein for the sequencelisting.

Table 3: Viral gene for arrays. Viral genomes were used to designoligonucleotides for the microarrays. The accession numbers for theviral genomes used are given, along with the gene name and location ofthe region used for oligonucleotide design.

Table 4. Dependent variables for discovery of gene expression markers ofcardiac allograft rejection. A stable Grade 0 is a Grade 0 biopsy in apatient who does not experience rejection with the subsequent biopsy. HGor highest grade means that the higher of the biopsy grades from thecentralized and local pathologists was used for a definition of thedependent variable.

Table 5: Real-time PCR assay reporter and quencher dyes. Variouscombinations of reporter and quencher dyes are useful for real-time PCRassays. Reporter and quencher dyes work optimally in specificcombinations defined by their spectra. For each reporter, appropriatechoices for quencher dyes are given.

Table 6: Rejection marker PCR assay results

Results of real-time PCR assays are listed for the comparison ofrejection samples to no rejection samples. The fold change is given forexpression of each gene in rejection/no rejection samples. The p-valuefor the t-test comparing the rejection and no rejection classes isgiven.

Table 7: Summary results of array rejection significance analysis.Summary results are given for correlation analysis of leukocyte geneexpression to acute rejection using significance analysis formicroarrays (SAM). Five analyses are described. The ISHLT grades used todefine the rejection and no rejection classes are given. In each casethe highest grade from three pathology reading was taken for analysis.All samples are used for two analyses. The other analyses reduceredundancy of patients used in the analysis by using only one sample perpatient (“Non-redundant”) or using only one sample per patient within agiven class (“Non-redundant within class”). The number of samples usedin the analysis is given and the lowest false detection rate (FDR)achieved is noted.

Table 8: Renal tissue rejection array significance analysis. Genes arelisted that were identified as upregulated using microarrays on renaltissue with acute rejection versus controls. Significance analysis formicroarrays (SAM) was used to determine the false detection rate foreach gene (FDR). Genes with known expression in leukocytes are noted inthe table.

Table 9: Rejection marker sequence analysis. For 63 of the allograftrejection markers listed in Table 2, an analysis of the gene sequencewas done. The genes and proteins are identified by accession numbers.The cellular localization of each gene is described as either secreted,nuclear, mitochondrial, cytoplasmic or cellular membrane. The functionof the gene is also described.

Table 10: Gene expression markers for immature cells of a variety oflineages are given in Table 10 by way of example

Table 11: Changes in the rate of hematopoiesis have been correlated to anumber of disease states and other pathologies. Examples of suchconditions are listed in Table 11.

Table 12: This table lists the oligonucleotides and associated genesidentified as having value for the diagnosis and monitoring of CMVinfection. The first column gives the SEQ ID that corresponds to theoligonucleotide in the sequence listing. The unigene number, genebankaccession and GI number are also given for each sequence when known. Thename of the gene associated with the accession number is noted. Thestrand is noted as −1 or 1, meaning that the probe was designed from thecomplement of the sequence (−1) or directly from the sequence (1). Next,the nucleotide sequence of each probe is also given. For each gene, thefalse detection rate (FDR) from the significance analysis described inexample 7 is given if applicable. WBC is the white blood cell count. WPTis the number of weeks past transplant.

DETAILED DESCRIPTION OF THE INVENTION Definitions

Unless defined otherwise, all scientific and technical terms areunderstood to have the same meaning as commonly used in the art to whichthey pertain. For the purpose of the present invention, the followingterms are defined below.

In the context of the invention, the term “gene expression system”refers to any system, device or means to detect gene expression andincludes diagnostic agents, candidate libraries, oligonucleotide sets orprobe sets.

The term “monitoring” is used herein to describe the use of gene sets toprovide useful information about an individual or an individual's healthor disease status. “Monitoring” can include, determination of prognosis,risk-stratification, selection of drug therapy, assessment of ongoingdrug therapy, prediction of outcomes, determining response to therapy,diagnosis of a disease or disease complication, following progression ofa disease or providing any information relating to a patients healthstatus over time, selecting patients most likely to benefit fromexperimental therapies with known molecular mechanisms of action,selecting patients most likely to benefit from approved drugs with knownmolecular mechanisms where that mechanism may be important in a smallsubset of a disease for which the medication may not have a label,screening a patient population to help decide on a moreinvasive/expensive test, for example a cascade of tests from anon-invasive blood test to a more invasive option such as biopsy, ortesting to assess side effects of drugs used to treat anotherindication.

The term “diagnostic oligonucleotide set” generally refers to a set oftwo or more oligonucleotides that, when evaluated for differentialexpression of their products, collectively yields predictive data. Suchpredictive data typically relates to diagnosis, prognosis, monitoring oftherapeutic outcomes, and the like. In general, the components of adiagnostic oligonucleotide set are distinguished from nucleotidesequences that are evaluated by analysis of the DNA to directlydetermine the genotype of an individual as it correlates with aspecified trait or phenotype, such as a disease, in that it is thepattern of expression of the components of the diagnostic nucleotideset, rather than mutation or polymorphism of the DNA sequence thatprovides predictive value. It will be understood that a particularcomponent (or member) of a diagnostic nucleotide set can, in some cases,also present one or more mutations, or polymorphisms that are amenableto direct genotyping by any of a variety of well known analysis methods,e.g., Southern blotting, RFLP, AFLP, SSCP, SNP, and the like.

A “disease specific target oligonucleotide sequence” is a gene or otheroligonucleotide that encodes a polypeptide, most typically a protein, ora subunit of a multi-subunit protein, that is a therapeutic target for adisease, or group of diseases.

A “candidate library” or a “candidate oligonucleotide library” refers toa collection of oligonucleotide sequences (or gene sequences) that byone or more criteria have an increased probability of being associatedwith a particular disease or group of diseases. The criteria can be, forexample, a differential expression pattern in a disease state or inactivated or resting leukocytes in vitro as reported in the scientificor technical literature, tissue specific expression as reported in asequence database, differential expression in a tissue or cell type ofinterest, or the like. Typically, a candidate library has at least 2members or components; more typically, the library has in excess ofabout 10, or about 100, or about 1000, or even more, members orcomponents.

The term “disease criterion” is used herein to designate an indicator ofa disease, such as a diagnostic factor, a prognostic factor, a factorindicated by a medical or family history, a genetic factor, or asymptom, as well as an overt or confirmed diagnosis of a diseaseassociated with several indicators such as those selected from the abovelist. A disease criterion includes data describing a patient's healthstatus, including retrospective or prospective health data, e.g. in theform of the patient's medical history, laboratory test results,diagnostic test result, clinical events, medications, lists, response(s)to treatment and risk factors, etc.

The terms “molecular signature” or “expression profile” refers to thecollection of expression values for a plurality (e.g., at least 2, butfrequently about 10, about 100, about 1000, or more) of members of acandidate library. In many cases, the molecular signature represents theexpression pattern for all of the nucleotide sequences in a library orarray of candidate or diagnostic nucleotide sequences or genes.Alternatively, the molecular signature represents the expression patternfor one or more subsets of the candidate library. The term“oligonucleotide” refers to two or more nucleotides. Nucleotides may beDNA or RNA, naturally occurring or synthetic.

The term “healthy individual,” as used herein, is relative to aspecified disease or disease criterion. That is, the individual does notexhibit the specified disease criterion or is not diagnosed with thespecified disease. It will be understood, that the individual inquestion, can, of course, exhibit symptoms, or possess various indicatorfactors for another disease.

Similarly, an “individual diagnosed with a disease” refers to anindividual diagnosed with a specified disease (or disease criterion).Such an individual may, or may not, also exhibit a disease criterionassociated with, or be diagnosed with another (related or unrelated)disease.

An “array” is a spatially or logically organized collection, e.g., ofoligonucleotide sequences or nucleotide sequence products such as RNA orproteins encoded by an oligonucleotide sequence. In some embodiments, anarray includes antibodies or other binding reagents specific forproducts of a candidate library.

When referring to a pattern of expression, a “qualitative” difference ingene expression refers to a difference that is not assigned a relativevalue. That is, such a difference is designated by an “all or nothing”valuation. Such an all or nothing variation can be, for example,expression above or below a threshold of detection (an on/off pattern ofexpression). Alternatively, a qualitative difference can refer toexpression of different types of expression products, e.g., differentalleles (e.g., a mutant or polymorphic allele), variants (includingsequence variants as well as post-translationally modified variants),etc.

In contrast, a “quantitative” difference, when referring to a pattern ofgene expression, refers to a difference in expression that can beassigned a value on a graduated scale, (e.g., a 0-5 or 1-10 scale, a+−+++ scale, a grade 1-grade 5 scale, or the like; it will be understoodthat the numbers selected for illustration are entirely arbitrary and inno-way are meant to be interpreted to limit the invention).

Gene Expression Systems of the Invention

The invention is directed to a gene expression system having one or moreDNA molecules wherein the one or more DNA molecules has a nucleotidesequence which detects expression of a gene corresponding to theoligonucleotides depicted in the Sequence Listing. In one format, theoligonucleotide detects expression of a gene that is differentiallyexpressed in leukocytes. The gene expression system may be a candidatelibrary, a diagnostic agent, a diagnostic oligonucleotide set or adiagnostic probe set. The DNA molecules may be genomic DNA, proteinnucleic acid (PNA), cDNA or synthetic oligonucleotides. Following theprocedures taught herein, one can identity sequences of interest foranalyzing gene expression in leukocytes. Such sequences may bepredictive of a disease state.

Diagnostic Oligonucleotides of the Invention

The invention relates to diagnostic nucleotide set(s) comprising membersof the leukocyte candidate library listed in Table 2, Table 8, and inthe Sequence Listing, for which a correlation exists between the healthstatus of an individual, the individual's expression of RNA or proteinproducts corresponding to the nucleotide sequence, and the diagnosis andprognosis of transplant rejection. In some instances, only oneoligonucleotide is necessary for such detection. Members of a diagnosticoligonucleotide set may be identified by any means capable of detectingexpression of RNA or protein products, including but not limited todifferential expression screening, PCR, RT-PCR, SAGE analysis,high-throughput sequencing, microarrays, liquid or other arrays,protein-based methods (e.g., western blotting, proteomics, and othermethods described herein), and data mining methods, as further describedherein.

In one embodiment, a diagnostic oligonucleotide set comprises at leasttwo oligonucleotide sequences listed in Table 2, Table 8, or theSequence Listing which are differentially expressed in leukocytes in anindividual with at least one disease criterion for at least oneleukocyte-implicated disease relative to the expression in individualwithout the at least one disease criterion, wherein expression of thetwo or more nucleotide sequences is correlated with at least one diseasecriterion, as described below.

In another embodiment, a diagnostic nucleotide set comprises at leastone oligonucleotide having an oligonucleotide sequence listed in Table2, Table 8, or the Sequence Listing which is differentially expressed,and further wherein the differential expression/correlation has notpreviously been described. In some embodiments, the diagnosticnucleotide set is immobilized on an array.

In another embodiment, diagnostic nucleotides (or nucleotide sets) arerelated to the members of the leukocyte candidate library listed inTable 2, Table 8, or in the Sequence Listing, for which a correlationexists between the health status, diagnosis and prognosis of transplantrejection (or disease criterion) of an individual. The diagnosticnucleotides are partially or totally contained in (or derived from)full-length gene sequences (or predicted full-length gene sequences) forthe members of the candidate library listed in Table 2, Table 8, and thesequence listing. In some cases, oligonucleotide sequences are designedfrom EST or Chromosomal sequences from a public database. In these casesthe full-length gene sequences may not be known. Full-length sequencesin these cases can be predicted using gene prediction algorithms.Alternatively the full-length can be determined by cloning andsequencing the full-length gene or genes that contain the sequence ofinterest using standard molecular biology approaches described here. Thesame is true for oligonucleotides designed from our sequencing of cDNAlibraries where the cDNA does not match any sequence in the publicdatabases.

The diagnostic nucleotides may also be derived from other genes that arecoexpressed with the correlated sequence or full-length gene. Genes mayshare expression patterns because they are regulated in the samemolecular pathway. Because of the similarity of expression behaviorgenes are identified as surrogates in that they can substitute for adiagnostic gene in a diagnostic gene set. Example 4 demonstrates thediscovery of surrogates from the data and the sequence listingidentifies and gives the sequence for surrogates for cardiac diagnosticgenes.

As used herein the term “gene cluster” or “cluster” refers to a group ofgenes related by expression pattern. In other words, a cluster of genesis a group of genes with similar regulation across different conditions,such as graft non-rejection versus graft rejection. The expressionprofile for each gene in a cluster should be correlated with theexpression profile of at least one other gene in that cluster.Correlation may be evaluated using a variety of statistical methods. Asused herein the term “surrogate” refers to a gene with an expressionprofile such that it can substitute for a diagnostic gene in adiagnostic assay. Such genes are often members of the same gene clusteras the diagnostic gene. For each member of a diagnostic gene set, a setof potential surrogates can be identified through identification ofgenes with similar expression patterns as described below.

Many statistical analyses produce a correlation coefficient to describethe relatedness between two gene expression patterns. Patterns may beconsidered correlated if the correlation coefficient is greater than orequal to 0.8. In preferred embodiments, the correlation coefficientshould be greater than 0.85, 0.9 or 0.95. Other statistical methodsproduce a measure of mutual information to describe the relatednessbetween two gene expression patterns. Patterns may be consideredcorrelated if the normalized mutual information value is greater than orequal to 0.7. In preferred embodiments, the normalized mutualinformation value should be greater than 0.8, 0.9 or 0.95. Patterns mayalso be considered similar if they cluster closely upon hierarchicalclustering of gene expression data (Eisen et al. 1998). Similar patternsmay be those genes that are among the 1, 2, 5, 10, 20, 50 or 100 nearestneighbors in a hierarchical clustering or have a similarity score (Eisenet al. 1998) of >0.5, 0.7, 0.8, 0.9, 0.95 or 0.99. Similar patterns mayalso be identified as those genes found to be surrogates in aclassification tree by CART (Breiman et al. 1994). Often, but notalways, members of a gene cluster have similar biological functions inaddition to similar gene expression patterns.

Correlated genes, clusters and surrogates are identified for thediagnostic genes of the invention. These surrogates may be used asdiagnostic genes in an assay instead of, or in addition to, thediagnostic genes for which they are surrogates.

The invention also provides diagnostic probe sets. It is understood thata probe includes any reagent capable of specifically identifying anucleotide sequence of the diagnostic nucleotide set, including but notlimited to amplified DNA, amplified RNA, cDNA, syntheticoligonucleotide, partial or full-length nucleic acid sequences. Inaddition, the probe may identify the protein product of a diagnosticnucleotide sequence, including, for example, antibodies and otheraffinity reagents.

It is also understood that each probe can correspond to one gene, ormultiple probes can correspond to one gene, or both, or one probe cancorrespond to more than one gene.

Homologs and variants of the disclosed nucleic acid molecules may beused in the present invention. Homologs and variants of these nucleicacid molecules will possess a relatively high degree of sequenceidentity when aligned using standard methods. The sequences encompassedby the invention have at least 40-50, 50-60, 70-80, 80-85, 85-90, 90-95or 95-100% sequence identity to the sequences disclosed herein.

It is understood that for expression profiling, variations in thedisclosed sequences will still permit detection of gene expression. Thedegree of sequence identity required to detect gene expression variesdepending on the length of the oligomer. For a 60 mer, 6-8 randommutations or 6-8 random deletions in a 60 mer do not affect geneexpression detection. Hughes, T R, et al. “Expression profiling usingmicroarrays fabricated by an ink-jet oligonucleotide synthesizer. NatureBiotechnology, 19:343-347(2001). As the length of the DNA sequence isincreased, the number of mutations or deletions permitted while stillallowing gene expression detection is increased.

As will be appreciated by those skilled in the art, the sequences of thepresent invention may contain sequencing errors. That is, there may beincorrect nucleotides, frameshifts, unknown nucleotides, or other typesof sequencing errors in any of the sequences; however, the correctsequences will fall within the homology and stringency definitionsherein.

The minimum length of an oligonucleotide probe necessary for specifichybridization in the human genome can be estimated using two approaches.The first method uses a statistical argument that the probe will beunique in the human genome by chance. Briefly, the number of independentperfect matches (Po) expected for an oligonucleotide of length L in agenome of complexity C can be calculated from the equation (Laird C D,Chromosoma 32:378 (1971):Po=(¼)^(L)*2C

In the case of mammalian genomes, 2C=˜3.6×10⁹, and an oligonucleotide of14-15 nucleotides is expected to be represented only once in the genome.However, the distribution of nucleotides in the coding sequence ofmammalian genomes is nonrandom (Lathe, R. J. Mol. Biol. 183:1 (1985) andlonger oligonucleotides may be preferred in order to in increase thespecificity of hybridization. In practical terms, this works out toprobes that are 19-40 nucleotides long (Sambrook J et al., infra). Thesecond method for estimating the length of a specific probe is to use aprobe long enough to hybridize under the chosen conditions and use acomputer to search for that sequence or close matches to the sequence inthe human genome and choose a unique match. Probe sequences are chosenbased on the desired hybridization properties as described in Chapter 11of Sambrook et al, infra. The PRIMER3 program is useful for designingthese probes (S. Rozen and H. Skaletsky 1996, 1997; Primer3 codeavailable at the web site located atgenome.wi.mit.edu/genome_software/other/primer3.html). The sequences ofthese probes are then compared pair wise against a database of the humangenome sequences using a program such as BLAST or MEGABLAST (Madden, T.L et al. (1996) Meth. Enzymol. 266:131-141). Since most of the humangenome is now contained in the database, the number of matches will bedetermined. Probe sequences are chosen that are unique to the desiredtarget sequence.

In some embodiments, a diagnostic probe set is immobilized on an array.The array is optionally comprises one or more of: a chip array, a platearray, a bead array, a pin array, a membrane array, a solid surfacearray, a liquid array, an oligonucleotide array, a polynucleotide arrayor a cDNA array, a microtiter plate, a pin array, a bead array, amembrane or a chip.

In some embodiments, the leukocyte-implicated disease is selected fromthe diseases listed in Table 1. In other embodiments, In someembodiments, the disease is atherosclerosis or cardiac allograftrejection. In other embodiments, the disease is congestive heartfailure, angina, and myocardial infarction.

In some embodiments, diagnostic nucleotides of the invention are used asa diagnostic gene set in combination with genes that are know to beassociated with a disease state (“known markers”). The use of thediagnostic nucleotides in combination with the known markers can provideinformation that is not obtainable through the known markers alone. Theknown markers include those identified by the prior art listingprovided.

Hematopoeisis

The present invention is also directed to methods of measurement of therate of hematopoiesis using the diagnostic oligonucleotides of theinvention and measurement of the rates of hematopoeisis by any techniqueas a method for the monitoring and diagnosis of transplant rejection.Precursor and immature cells often have cell specific phenotypicmarkers. These are genes and/or proteins that expressed in a restrictedmanner in immature or precursor cells. This expression decreases withmaturation. Gene expression markers for immature cells of a variety oflineages are given in Table 10 below by way of example.

TABLE 10 Gene Cell type CD10 B-lymphoblasts RAG1 B-lymphoblasts RAG2B-lymphoblasts NF-E2 Platelets/Megakaryocyte/Erythroid GATA-1Platelets/Megakaryocyte GP IIb Platelets pf4 Platelets EPO-RErythroblast Band 4.1 Erythrocyte ALAS2 Erythroid specific hemebiosynthesis hemoglobin chains Erythocyte 2,3-BPG mutase ErythrocyteCD16b Neutrophil LAP Neutrophil CD16 NK cells CD159a NK cells

By measuring the levels of these and other genes in peripheral bloodsamples, an assessment of the number and proportion of immature orprecursor cells can be made. Of particular use is RNA quantification inerythrocytes and platelets. These cells are anucleated in their matureforms. During development, platelets pinch off of a megakaryocyte andtake a compliment of RNA without a nucleus. This RNA is quickly consumedby the platelet. Erythrocytes start as nucleated cells, but the nucleusextrudes toward the end of the maturation process. These cells have RNAwhich is rapidly consumed within the first 2 days of the cells 120 daylife span.

For these anucleated cell types, gene expression markers must bespecific only to the cell line (and not the immature form) to be usefulas measures of cellular production rates. Genes specific to the lineagevs. other blood cell types will serve as markers of cellular productionrates when measured on the RNA level. This is because RNA is specific toimmature forms in these cases. For example, hemoglobin is specific toerythrocytes, but hemoglobin RNA is specific to newly producederythrocytes. Therefore, if the rate of production of erythrocytesincreases, so will the level of a lineage specific RNA (e.g.,hemoglobin).

Hematopoietic growth factors and cytokines have incomplete lineagespecificity. G-CSF is administered to patient with low granulocytecounts and the effect is a stimulation of all lineages (granulocytes,erythrocytes, platelets, etc. . . . ). Hemolytic anemia leads toincreased production of multiple cell lineages although the only lineagein increased demand is the erythrocyte. Because of this lack ofspecificity of hematopoietic responses, erythrocyte and plateletproduction rates may serve as surrogates of increased production oflymphocyte lineages. Using RBCs and platelets production rates assurrogates for lymphocyte lineages may be useful because of the lack ofa nucleus in these cells and the ease of measuring cellular productionrates by simply measuring lineage specific RNA levels.

Hematopoiesis rates can be measured using gene expression profiling ofperipheral blood. RBC and platelet specific genes provide uniqueopportunity for this because of their lack of a nucleus and kinetics.New cells=new/much more RNA from these cell types in peripheral blood.Immature lymphocytes may be even more specific for immune activation andrejection. Cell specific markers of lymphocyte precursors wereidentified (aka lymphoblasts) see below. Granulocyte precursors andmarkers of megakaryocytes or premature forms of any blood cells may beuseful in this regard.

Applications for Measuring the Rate of Hematopoiesis

Changes in the rate of hematopoiesis have been correlated to a number ofdisease states and other pathologies. Examples of such conditions arelisted in Table 11. One of skill in the art would be aware of other suchconditions. In addition, one aspect of the present invention is theidentification of the linkage between changes in the rate ofhematopoiesis. The methods of the present invention directed tomeasuring the rates of hematopoiesis can therefore be applied to thediagnosis and monitoring of a number of disease states and otherpathologies. In addition, these methods can be beneficial in determiningappropriate therapies for patients.

TABLE 11 Cell Disorder/condition Cell type production Therapy Anemia -Iron Erythrocyte Decreased Iron Deficiency Anemia - B12, ErythrocyteDecreased B12, Folate Folate deficiency Anemia - Aplastic ErythrocyteDecreased Epogen, transfusion Anemia - hemolytic Erythrocyte IncreasedImmunosuppression, Splenectomy Anemia - Renal Erythrocyte DecreasedErythropoietin failure Anemia - Chronic Erythrocyte Decreased Treatunderlying disease cause Polycythemia rubra Erythrocyte Increased veraIdiophic Platelet Increased Immunosuppression, ThrrombocytopenicSplenectomy purpura Thrombotic Platelet Increased or Immunosuppression,Thrombocytopenic decreased plasmapheresis purpura Essential PlateletIncreased thrombocytosis Leukemia All lineages, Increase, Chemotherapy,variable decreased or BMT abnomal Cytopenias due to All lineages,Decreased Epo, neupogen immunosupression variable Cytopenias due to Alllineages, Decreased Epo, GCSF, Chemotherapy variable GMCSF GVHD Alllineages, Decreased Immunosuppression variable Myelodysplasia Alllineages, Decreased, Chemo? variable increased or abnormal Allograftrejection Lymphocytes, Increased Immunosuppression All lineagesAutoimmune Lymphocytes, Increased Immunosuppression diseases Alllineages (many)

The methods of the present invention are also useful for monitoringtreatment regimens of diseases or other pathologies which are correlatedwith changes in the rate of hematopoiesis. Furthermore, the methods maybe used to monitor treatment with agents that affect the rate ofhematopoiesis. One of skill in the art is aware of many such agents. Thefollowing agents are examples of such.

Erythropoietin is a growth factor that is used to treat a variety ofanemias that are due to decreased red cell production. Monitoring of redcell production by gene expression or other means may improve dosing andprovide a means for earlier assessment of response to therapy for thisexpensive drug.

Neupogen (G-CSF) is used for the treatment of low neutrophil counts(neutropenia) usually related to immunosuppression or chemotherapy.Monitoring neutrophil production by gene expression testing or anothermeans may improve dosing, patient selection, and shorten duration oftherapy.

Prednisone/Immunosuppression—One of most common side effects ofimmunosuppression is suppression of hematopoiesis. This may occur in anycell lineage. Gene expression monitoring or other measures ofhematopoietic rates could be used to monitor regularly for cytopenias ina particular cell line and the information could be used to modifydosing, modify therapy or add a specific hematologic growth factor.Following cell counts themselves is less sensitive and results in theneed for prolonged trials of therapies at a given dose before efficacyand toxicity can be assessed.

Monitoring of chemotherapeutic agents—Most chemotherapy agents suppressthe bone marrow for some or all lineages. Gene expression testing orother means of assessing hematopoietic rates could be used to monitorregularly for cytopenias in a particular cell line and use informationto modify dosing, modify therapy or add a specific hematologic growthfactor.

General Molecular Biology References

In the context of the invention, nucleic acids and/or proteins aremanipulated according to well known molecular biology techniques.Detailed protocols for numerous such procedures are described in, e.g.,in Ausubel et al. Current Protocols in Molecular Biology (supplementedthrough 2000) John Wiley & Sons, New York (“Ausubel”); Sambrook et al.Molecular Cloning—A Laboratory Manual (2nd Ed.), Vol. 1-3, Cold SpringHarbor Laboratory, Cold Spring Harbor, N.Y., 1989 (“Sambrook”), andBerger and Kimmel Guide to Molecular Cloning Techniques, Methods inEnzymology volume 152 Academic Press, Inc., San Diego, Calif.(“Berger”).

In addition to the above references, protocols for in vitroamplification techniques, such as the polymerase chain reaction (PCR),the ligase chain reaction (LCR), Q-replicase amplification, and otherRNA polymerase mediated techniques (e.g., NASBA), useful e.g., foramplifying cDNA probes of the invention, are found in Mullis et al.(1987) U.S. Pat. No. 4,683,202; PCR Protocols A Guide to Methods andApplications (Innis et al. eds) Academic Press Inc. San Diego, Calif.(1990) (“Innis”); Arnheim and Levinson (1990) C&EN 36; The Journal OfNIH Research (1991) 3:81; Kwoh et al. (1989) Proc Natl Acad Sci USA 86,1173; Guatelli et al. (1990) Proc Natl Acad Sci USA 87:1874; Lomell etal. (1989) J Clin Chem 35:1826; Landegren et al. (1988) Science241:1077; Van Brunt (1990) Biotechnology 8:291; Wu and Wallace (1989)Gene 4: 560; Barringer et al. (1990) Gene 89:117, and Sooknanan andMalek (1995) Biotechnology 13:563. Additional methods, useful forcloning nucleic acids in the context of the present invention, includeWallace et al. U.S. Pat. No. 5,426,039. Improved methods of amplifyinglarge nucleic acids by PCR are summarized in Cheng et al. (1994) Nature369:684 and the references therein.

Certain polynucleotides of the invention, e.g., oligonucleotides can besynthesized utilizing various solid-phase strategies involvingmononucleotide- and/or trinucleotide-based phosphoramidite couplingchemistry. For example, nucleic acid sequences can be synthesized by thesequential addition of activated monomers and/or trimers to anelongating polynucleotide chain. See e.g., Caruthers, M. H. et al.(1992) Meth Enzymol 211:3.

In lieu of synthesizing the desired sequences, essentially any nucleicacid can be custom ordered from any of a variety of commercial sources,such as The Midland Certified Reagent Company, The Great American GeneCompany ExpressGen, Inc., Operon Technologies, Inc. and many others.

Similarly, commercial sources for nucleic acid and protein microarraysare available, and include, e.g., Agilent Technologies, Palo Alto,Calif. Affymetrix, Santa Clara, Calif.; and others.

One area of relevance to the present invention is hybridization ofoligonucleotides. Those of skill in the art differentiate hybridizationconditions based upon the stringency of hybridization. For example,highly stringent conditions could include hybridization to filter-boundDNA in 0.5 M NaHPO₄, 7% sodium dodecyl sulfate (SDS), 1 mM EDTA at 65°C., and washing in 0.1×SSC/0.1% SDS at 68° C. (Ausubel F. M. et al.,eds., 1989, Current Protocols in Molecular Biology, Vol. I, GreenPublishing Associates, Inc., and John Wiley & sons, Inc., New York, atp. 2.10.3). Moderate stringency conditions could include, e.g., washingin 0.2×SSC/0.1% SDS at 42° C. (Ausubel et al., 1989, supra).

The invention also includes nucleic acid molecules, preferably DNAmolecules, that hybridize to, and are therefore the complements of, theDNA sequences of the present invention. Such hybridization conditionsmay be highly stringent or less highly stringent, as described above. Ininstances wherein the nucleic acid molecules are deoxyoligonucleotides(“oligos”), highly stringent conditions may refer, e.g., to washing in6×SSC/0.05% sodium pyrophosphate at 37° C. (for 14-base oligos), 48° C.(for 17-base oligos), 55° C. (for 20-base oligos), and 60° C. (for23-base oligos). These nucleic acid molecules may act as targetnucleotide sequence antisense molecules, useful, for example, in targetnucleotide sequence regulation and/or as antisense primers inamplification reactions of target nucleotide sequence nucleic acidsequences. Further, such sequences may be used as part of ribozymeand/or triple helix sequences, also useful for target nucleotidesequence regulation. Still further, such molecules may be used ascomponents of diagnostic methods whereby the presence of adisease-causing allele, may be detected.

Identification of Diagnostic Nucleotide Sets

Candidate Library

Libraries of candidates that are differentially expressed in leukocytesare substrates for the identification and evaluation of diagnosticoligonucleotide sets and disease specific target nucleotide sequences.

The term leukocyte is used generically to refer to any nucleated bloodcell that is not a nucleated erythrocyte. More specifically, leukocytescan be subdivided into two broad classes. The first class includesgranulocytes, including, most prevalently, neutrophils, as well aseosinophils and basophils at low frequency. The second class, thenon-granular or mononuclear leukocytes, includes monocytes andlymphocytes (e.g., T cells and B cells). There is an extensiveliterature in the art implicating leukocytes, e.g., neutrophils,monocytes and lymphocytes in a wide variety of disease processes,including inflammatory and rheumatic diseases, neurodegenerativediseases (such as Alzheimer's dementia), cardiovascular disease,endocrine diseases, transplant rejection, malignancy and infectiousdiseases, and other diseases listed in Table 1. Mononuclear cells areinvolved in the chronic immune response, while granulocytes, which makeup approximately 60% of the leukocytes, have a non-specific andstereotyped response to acute inflammatory stimuli and often have a lifespan of only 24 hours.

In addition to their widespread involvement and/or implication innumerous disease related processes, leukocytes are particularlyattractive substrates for clinical and experimental evaluation for avariety of reasons. Most importantly, they are readily accessible at lowcost from essentially every potential subject. Collection is minimallyinvasive and associated with little pain, disability or recovery time.Collection can be performed by minimally trained personnel (e.g.,phlebotomists, medical technicians, etc.) in a variety of clinical andnon-clinical settings without significant technological expenditure.Additionally, leukocytes are renewable, and thus available at multipletime points for a single subject.

Assembly of an Initial Candidate Library

The initial candidate library was assembled from a combination of“mining” publication and sequence databases and construction of adifferential expression library. Candidate oligonucleotide sequences inthe library may be represented by a full-length or partial nucleic acidsequence, deoxyribonucleic acid (DNA) sequence, cDNA sequence, RNAsequence, synthetic oligonucleotides, etc. The nucleic acid sequence canbe at least 19 nucleotides in length, at least 25 nucleotides, at least40 nucleotides, at least 100 nucleotides, or larger. Alternatively, theprotein product of a candidate nucleotide sequence may be represented ina candidate library using standard methods, as further described below.In selecting and validatating diagnostic oligonucleotides, an initiallibrary of 8,031 candidate oligonucleotide sequences using nucleic acidsequences of 50 nucleotides in length was constructed as describedbelow.

Candidate Nucleotide Library of the Invention

We identified members of an initial candidate nucleotide library thatare differentially expressed in activated leukocytes and restingleukocytes. From that initial candidate nucleotide library, a pool ofcandidates was selected as listed in Table 2, Table 8, and the sequencelisting. Accordingly, the invention provides the candidate leukocytenucleotide library comprising the nucleotide sequences listed in Table2, Table 8, and in the sequence listing. In another embodiment, theinvention provides an candidate library comprising at least onenucleotide sequence listed in Tables 2 and 8 and the sequence listing.In another embodiment, the invention provides an candidate librarycomprising at least two nucleotide sequences listed in Tables 2 and 8and the sequence listing. In another embodiment, the at least twonucleotide sequence are at least 19 nucleotides in length, at least 35nucleotides, at least 40 nucleotides or at least 100 nucleotides. Insome embodiments, the nucleotide sequences comprises deoxyribonucleicacid (DNA) sequence, ribonucleic acid (RNA) sequence, syntheticoligonucleotide sequence, or genomic DNA sequence. It is understood thatthe nucleotide sequences may each correspond to one gene, or thatseveral nucleotide sequences may correspond to one gene, or both.

The invention also provides probes to the candidate nucleotide library.In one embodiment of the invention, the probes comprise at least twonucleotide sequences listed in Table 2, Table 8, or the sequence listingwhich are differentially expressed in leukocytes in an individual with aleast one disease criterion for at least one leukocyte-related diseaseand in leukocytes in an individual without the at least one diseasecriterion, wherein expression of the two or more nucleotide sequences iscorrelated with at least one disease criterion. It is understood that aprobe may detect either the RNA expression or protein product expressionof the candidate nucleotide library. Alternatively, or in addition, aprobe can detect a genotype associated with a candidate nucleotidesequence, as further described below. In another embodiment, the probesfor the candidate nucleotide library are immobilized on an array.

The candidate nucleotide library of the invention is useful inidentifying diagnostic nucleotide sets of the invention and is itself adiagnostic nucleotide set of the invention, as described below. Thecandidate nucleotide sequences may be further characterized, and may beidentified as a disease target nucleotide sequence and/or a novelnucleotide sequence, as described below. The candidate nucleotidesequences may also be suitable for use as imaging reagents, as describedbelow.

Detection of Non-Leukocyte Expressed Genes

When measuring gene expression levels in a blood sample, RNAs may bemeasured that are not derived from leukocytes. Examples are viral genes,free RNAs that have been released from damaged non-leukocyte cell typesor RNA from circulating non-leukocyte cell types. For example, in theprocess of acute allograft rejection, tissue damage may result inrelease of allograft cells or RNAs derived from allograft cells into thecirculation. In the case of cardiac allografts, such transcripts may bespecific to muscle (myoglobin) or to cardiac muscle (Troponin I, ToponinT, CK-MB). Presence of cardiac specific mRNAs in peripheral blood mayindicate ongoing or recent cardiac cellular damage (resulting from acuterejection). Therefore, such genes may be excellent diagnostic markersfor allograft rejection.

Generation of Expression Patterns

RNA, DNA or Protein Sample Procurement

Following identification or assembly of a library of differentiallyexpressed candidate nucleotide sequences, leukocyte expression profilescorresponding to multiple members of the candidate library are obtained.Leukocyte samples from one or more subjects are obtained by standardmethods. Most typically, these methods involve trans-cutaneous venoussampling of peripheral blood. While sampling of circulating leukocytesfrom whole blood from the peripheral vasculature is generally thesimplest, least invasive, and lowest cost alternative, it will beappreciated that numerous alternative sampling procedures exist, and arefavorably employed in some circumstances. No pertinent distinctionexists, in fact, between leukocytes sampled from the peripheralvasculature, and those obtained, e.g., from a central line, from acentral artery, or indeed from a cardiac catheter, or during a surgicalprocedure which accesses the central vasculature. In addition, otherbody fluids and tissues that are, at least in part, composed ofleukocytes are also desirable leukocyte samples. For example, fluidsamples obtained from the lung during bronchoscopy may be rich inleukocytes, and amenable to expression profiling in the context of theinvention, e.g., for the diagnosis, prognosis, or monitoring of lungtransplant rejection, inflammatory lung diseases or infectious lungdisease. Fluid samples from other tissues, e.g., obtained by endoscopyof the colon, sinuses, esophagus, stomach, small bowel, pancreatic duct,biliary tree, bladder, ureter, vagina, cervix or uterus, etc., are alsosuitable. Samples may also be obtained other sources containingleukocytes, e.g., from urine, bile, cerebrospinal fluid, feces, gastricor intestinal secretions, semen, or solid organ or joint biopsies.

Most frequently, mixed populations of leukocytes, such as are found inwhole blood are utilized in the methods of the present invention. Acrude separation, e.g., of mixed leukocytes from red blood cells, and/orconcentration, e.g., over a sucrose, percoll or ficoll gradient, or byother methods known in the art, can be employed to facilitate therecovery of RNA or protein expression products at sufficientconcentrations, and to reduce non-specific background. In someinstances, it can be desirable to purify sub-populations of leukocytes,and methods for doing so, such as density or affinity gradients, flowcytometry, fluorescence Activated Cell Sorting (FACS), immuno-magneticseparation, “panning,” and the like, are described in the availableliterature and below.

Obtaining DNA, RNA and Protein Samples for Expression Profiling

Expression patterns can be evaluated at the level of DNA, or RNA orprotein products. For example, a variety of techniques are available forthe isolation of RNA from whole blood. Any technique that allowsisolation of mRNA from cells (in the presence or absence of rRNA andtRNA) can be utilized. In brief, one method that allows reliableisolation of total RNA suitable for subsequent gene expression analysis,is described as follows. Peripheral blood (either venous or arterial) isdrawn from a subject, into one or more sterile, endotoxin free, tubescontaining an anticoagulant (e.g., EDTA, citrate, heparin, etc.).Typically, the sample is divided into at least two portions. Oneportion, e.g., of 5-8 ml of whole blood is frozen and stored for futureanalysis, e.g., of DNA or protein. A second portion, e.g., ofapproximately 8 ml whole blood is processed for isolation of total RNAby any of a variety of techniques as described in, e.g., Sambook,Ausubel, below, as well as U.S. Pat. Nos. 5,728,822 and 4,843,155.

Typically, a subject sample of mononuclear leukocytes obtained fromabout 8 ml of whole blood, a quantity readily available from an adulthuman subject under most circumstances, yields 5-20 μg of total RNA.This amount is ample, e.g., for labeling and hybridization to at leasttwo probe arrays. Labeled probes for analysis of expression patterns ofnucleotides of the candidate libraries are prepared from the subject'ssample of RNA using standard methods. In many cases, cDNA is synthesizedfrom total RNA using a polyT primer and labeled, e.g., radioactive orfluorescent, nucleotides. The resulting labeled cDNA is then hybridizedto probes corresponding to members of the candidate nucleotide library,and expression data is obtained for each nucleotide sequence in thelibrary. RNA isolated from subject samples (e.g., peripheral bloodleukocytes, or leukocytes obtained from other biological fluids andsamples) is next used for analysis of expression patterns of nucleotidesof the candidate libraries.

In some cases, however, the amount of RNA that is extracted from theleukocyte sample is limiting, and amplification of the RNA is desirable.Amplification may be accomplished by increasing the efficiency of probelabeling, or by amplifying the RNA sample prior to labeling. It isappreciated that care must be taken to select an amplification procedurethat does not introduce any bias (with respect to gene expressionlevels) during the amplification process.

Several methods are available that increase the signal from limitingamounts of RNA, e.g. use of the Clontech (Glass Fluorescent LabelingKit) or Stratagene (Fairplay Microarray Labeling Kit), or the Micromaxkit (New England Nuclear, Inc.). Alternatively, cDNA is synthesized fromRNA using a T7-polyT primer, in the absence of label, and DNA dendrimersfrom Genisphere (3DNA Submicro) are hybridized to the poly T sequence onthe primer, or to a different “capture sequence” which is complementaryto a fluorescently labeled sequence. Each 3DNA molecule has 250fluorescent molecules and therefore can strongly label each cDNA.

Alternatively, the RNA sample is amplified prior to labeling Forexample, linear amplification may be performed, as described in U.S.Pat. No. 6,132,997. A T7-polyT primer is used to generate the cDNA copyof the RNA. A second DNA strand is then made to complete the substratefor amplification. The T7 promoter incorporated into the primer is usedby a T7 polymerase to produce numerous antisense copies of the originalRNA. Fluorescent dye labeled nucleotides are directly incorporated intothe RNA. Alternatively, amino allyl labeled nucleotides are incorporatedinto the RNA, and then fluorescent dyes are chemically coupled to theamino allyl groups, as described in Hughes. Other exemplary methods foramplification are described below.

It is appreciated that the RNA isolated must contain RNA derived fromleukocytes, but may also contain RNA from other cell types to a variabledegree. Additionally, the isolated RNA may come from subsets ofleukocytes, e.g. monocytes and/or T-lymphocytes, as described above.Such consideration of cell type used for the derivation of RNA depend onthe method of expression profiling used. Subsets of leukocytes can beobtained by fluorescence activated cell sorting (FACS), microfluidicscell separation systems or a variety of other methods. Cell sorting maybe necessary for the discovery of diagnostic gene sets, for theimplementation of gene sets as products or both. Cell sorting can beachieved with a variety of technologies (See Galbraith et al. 1999,Cantor et al. 1975, see also the technology of Guava Technologies,Hayward, Calif.).

DNA samples may be obtained for analysis of the presence of DNAmutations, single nucleotide polymorphisms (SNPs), or otherpolymorphisms. DNA is isolated using standard techniques, e.g. Maniatus,supra.

Expression of products of candidate nucleotides may also be assessedusing proteomics. Protein(s) are detected in samples of patient serum orfrom leukocyte cellular protein. Serum is prepared by centrifugation ofwhole blood, using standard methods. Proteins present in the serum mayhave been produced from any of a variety of leukocytes and non-leukocytecells, and include secreted proteins from leukocytes. Alternatively,leukocytes or a desired sub-population of leukocytes are prepared asdescribed above. Cellular protein is prepared from leukocyte samplesusing methods well known in the art, e.g., Trizol (Invitrogen LifeTechnologies, cat # 15596108; Chomczynski, P. and Sacchi, N. (1987)Anal. Biochem. 162, 156; Simms, D., Cizdziel, P. E., and Chomczynski, P.(1993) Focus® 15, 99; Chomczynski, P., Bowers-Finn, R., and Sabatini, L.(1987) J. of NIH Res. 6, 83; Chomczynski, P. (1993) Bio/Techniques 15,532; Bracete, A. M., Fox, D. K., and Simms, D. (1998) Focus 20, 82;Sewall, A. and McRae, S. (1998) Focus 20, 36; Anal Biochem 1984 April;138(1):141-3, A method for the quantitative recovery of protein indilute solution in the presence of detergents and lipids; Wessel D,Flugge U I. (1984) Anal Biochem. 1984 April; 138(1):141-143.

The assay itself may be a cell sorting assay in which cells are sortedand/or counted based on cell surface expression of a protein marker.(See Cantor et al. 1975, Galbraith et al 1999)

Obtaining Expression Patterns

Expression patterns, or profiles, of a plurality of nucleotidescorresponding to members of the candidate library are then evaluated inone or more samples of leukocytes. Typically, the leukocytes are derivedfrom patient peripheral blood samples, although, as indicated above,many other sample sources are also suitable. These expression patternsconstitute a set of relative or absolute expression values for a somenumber of RNAs or protein products corresponding to the plurality ofnucleotide sequences evaluated, which is referred to herein as thesubject's “expression profile” for those nucleotide sequences. Whileexpression patterns for as few as one independent member of thecandidate library can be obtained, it is generally preferable to obtainexpression patterns corresponding to a larger number of nucleotidesequences, e.g., about 2, about 5, about 10, about 20, about 50, about100, about 200, about 500, or about 1000, or more. The expressionpattern for each differentially expressed component member of thelibrary provides a finite specificity and sensitivity with respect topredictive value, e.g., for diagnosis, prognosis, monitoring, and thelike.

Clinical Studies, Data and Patient Groups

For the purpose of discussion, the term subject, or subject sample ofleukocytes, refers to an individual regardless of health and/or diseasestatus. A subject can be a patient, a study participant, a controlsubject, a screening subject, or any other class of individual from whoma leukocyte sample is obtained and assessed in the context of theinvention. Accordingly, a subject can be diagnosed with a disease, canpresent with one or more symptom of a disease, or a predisposing factor,such as a family (genetic) or medical history (medical) factor, for adisease, or the like. Alternatively, a subject can be healthy withrespect to any of the aforementioned factors or criteria. It will beappreciated that the term “healthy” as used herein, is relative to aspecified disease, or disease factor, or disease criterion, as the term“healthy” cannot be defined to correspond to any absolute evaluation orstatus. Thus, an individual defined as healthy with reference to anyspecified disease or disease criterion, can in fact be diagnosed withany other one or more disease, or exhibit any other one or more diseasecriterion.

Furthermore, while the discussion of the invention focuses, and isexemplified using human sequences and samples, the invention is equallyapplicable, through construction or selection of appropriate candidatelibraries, to non-human animals, such as laboratory animals, e.g., mice,rats, guinea pigs, rabbits; domesticated livestock, e.g., cows, horses,goats, sheep, chicken, etc.; and companion animals, e.g., dogs, cats,etc.

Methods for Obtaining Expression Data

Numerous methods for obtaining expression data are known, and any one ormore of these techniques, singly or in combination, are suitable fordetermining expression profiles in the context of the present invention.For example, expression patterns can be evaluated by northern analysis,PCR, RT-PCR, Taq Man analysis, FRET detection, monitoring one or moremolecular beacon, hybridization to an oligonucleotide array,hybridization to a cDNA array, hybridization to a polynucleotide array,hybridization to a liquid microarray, hybridization to a microelectricarray, molecular beacons, cDNA sequencing, clone hybridization, cDNAfragment fingerprinting, serial analysis of gene expression (SAGE),subtractive hybridization, differential display and/or differentialscreening (see, e.g., Lockhart and Winzeler (2000) Nature 405:827-836,and references cited therein).

For example, specific PCR primers are designed to a member(s) of ancandidate nucleotide library. cDNA is prepared from subject sample RNAby reverse transcription from a poly-dT oligonucleotide primer, andsubjected to PCR. Double stranded cDNA may be prepared using primerssuitable for reverse transcription of the PCR product, followed byamplification of the cDNA using in vitro transcription. The product ofin vitro transcription is a sense-RNA corresponding to the originalmember(s) of the candidate library. PCR product may, be also beevaluated in a number of ways known in the art, including real-timeassessment using detection of labeled primers, e.g. TaqMan or molecularbeacon probes. Technology platforms suitable for analysis of PCRproducts include the ABI 7700, 5700, or 7000 Sequence Detection Systems(Applied Biosystems, Foster City, Calif.), the MJ Research Opticon (MJResearch, Waltham, Mass.), the Roche Light Cycler (Roche Diagnositics,Indianapolis, Ind.), the Stratagene MX4000 (Stratagene, La Jolla,Calif.), and the Bio-Rad iCycler (Bio-Rad Laboratories, Hercules,Calif.). Alternatively, molecular beacons are used to detect presence ofa nucleic acid sequence in an unamplified RNA or cDNA sample, orfollowing amplification of the sequence using any method, e.g. IVT (InVitro transcription) or NASBA (nucleic acid sequence basedamplification). Molecular beacons are designed with sequencescomplementary to member(s) of an candidate nucleotide library, and arelinked to fluorescent labels. Each probe has a different fluorescentlabel with non-overlapping emission wavelengths. For example, expressionof ten genes may be assessed using ten different sequence-specificmolecular beacons.

Alternatively, or in addition, molecular beacons are used to assessexpression of multiple nucleotide sequences at once. Molecular beaconswith sequence complimentary to the members of a diagnostic nucleotideset are designed and linked to fluorescent labels. Each fluorescentlabel used must have a non-overlapping emission wavelength. For example,10 nucleotide sequences can be assessed by hybridizing 10 sequencespecific molecular beacons (each labeled with a different fluorescentmolecule) to an amplified or un-amplified RNA or cDNA sample. Such anassay bypasses the need for sample labeling procedures.

Alternatively, or in addition bead arrays can be used to assessexpression of multiple sequences at once. See, e.g., LabMAP 100, LuminexCorp, Austin, Tex.). Alternatively, or in addition electric arrays areused to assess expression of multiple sequences, as exemplified by thee-Sensor technology of Motorola (Chicago, Ill.) or Nanochip technologyof Nanogen (San Diego, Calif.)

Of course, the particular method elected will be dependent on suchfactors as quantity of RNA recovered, practitioner preference, availablereagents and equipment, detectors, and the like. Typically, however, theelected method(s) will be appropriate for processing the number ofsamples and probes of interest. Methods for high-throughput expressionanalysis are discussed below.

Alternatively, expression at the level of protein products of geneexpression is performed. For example, protein expression, in a sample ofleukocytes, can be evaluated by one or more method selected from among:western analysis, two-dimensional gel analysis, chromatographicseparation, mass spectrometric detection, protein-fusion reporterconstructs, colorimetric assays, binding to a protein array andcharacterization of polysomal mRNA. One particularly favorable approachinvolves binding of labeled protein expression products to an array ofantibodies specific for members of the candidate library. Methods forproducing and evaluating antibodies are widespread in the art, see,e.g., Coligan, supra; and Harlow and Lane (1989) Antibodies: ALaboratory Manual, Cold Spring Harbor Press, NY (“Harlow and Lane”).Additional details regarding a variety of immunological and immunoassayprocedures adaptable to the present invention by selection of antibodyreagents specific for the products of candidate nucleotide sequences canbe found in, e.g., Stites and Terr (eds.)(1991) Basic and ClinicalImmunology, 7^(th) ed., and Paul, supra. Another approach uses systemsfor performing desorption spectrometry. Commercially available systems,e.g., from Ciphergen Biosystems, Inc. (Fremont, Calif.) are particularlywell suited to quantitative analysis of protein expression. Indeed,Protein Chip® arrays (see, e.g., the web site ciphergen.com) used indesorption spectrometry approaches provide arrays for detection ofprotein expression. Alternatively, affinity reagents, e.g., antibodies,small molecules, etc.) are developed that recognize epitopes of theprotein product. Affinity assays are used in protein array assays, e.g.to detect the presence or absence of particular proteins. Alternatively,affinity reagents are used to detect expression using the methodsdescribed above. In the case of a protein that is expressed on the cellsurface of leukocytes, labeled affinity reagents are bound topopulations of leukocytes, and leukocytes expressing the protein areidentified and counted using fluorescent activated cell sorting (FACS).

It is appreciated that the methods of expression evaluation discussedherein, although discussed in the context of discovery of diagnosticnucleotide sets, are equally applicable for expression evaluation whenusing diagnostic nucleotide sets for, e.g. diagnosis of diseases, asfurther discussed below.

High Throughput Expression Assays

A number of suitable high throughput formats exist for evaluating geneexpression. Typically, the term high throughput refers to a format thatperforms at least about 100 assays, or at least about 500 assays, or atleast about 1000 assays, or at least about 5000 assays, or at leastabout 10,000 assays, or more per day. When enumerating assays, eitherthe number of samples or the number of candidate nucleotide sequencesevaluated can be considered. For example, a northern analysis of, e.g.,about 100 samples performed in a gridded array, e.g., a dot blot, usinga single probe corresponding to an candidate nucleotide sequence can beconsidered a high throughput assay. More typically, however, such anassay is performed as a series of duplicate blots, each evaluated with adistinct probe corresponding to a different member of the candidatelibrary. Alternatively, methods that simultaneously evaluate expressionof about 100 or more candidate nucleotide sequences in one or moresamples, or in multiple samples, are considered high throughput.

Numerous technological platforms for performing high throughputexpression analysis are known. Generally, such methods involve a logicalor physical array of either the subject samples, or the candidatelibrary, or both. Common array formats include both liquid and solidphase arrays. For example, assays employing liquid phase arrays, e.g.,for hybridization of nucleic acids, binding of antibodies or otherreceptors to ligand, etc., can be performed in multiwell, or microtiter,plates. Microtiter plates with 96, 384 or 1536 wells are widelyavailable, and even higher numbers of wells, e.g., 3456 and 9600 can beused. In general, the choice of microtiter plates is determined by themethods and equipment, e.g., robotic handling and loading systems, usedfor sample preparation and analysis. Exemplary systems include, e.g.,the ORCA™ system from Beckman-Coulter, Inc. (Fullerton, Calif.) and theZymate systems from Zymark Corporation (Hopkinton, Mass.).

Alternatively, a variety of solid phase arrays can favorably be employedin to determine expression patterns in the context of the invention.Exemplary formats include membrane or filter arrays (e.g.,nitrocellulose, nylon), pin arrays, and bead arrays (e.g., in a liquid“slurry”). Typically, probes corresponding to nucleic acid or proteinreagents that specifically interact with (e.g., hybridize to or bind to)an expression product corresponding to a member of the candidate libraryare immobilized, for example by direct or indirect cross-linking, to thesolid support. Essentially any solid support capable of withstanding thereagents and conditions necessary for performing the particularexpression assay can be utilized. For example, functionalized glass,silicon, silicon dioxide, modified silicon, any of a variety ofpolymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride,polystyrene, polycarbonate, or combinations thereof can all serve as thesubstrate for a solid phase array.

In a preferred embodiment, the array is a “chip” composed, e.g., of oneof the above specified materials. Polynucleotide probes, e.g., RNA orDNA, such as cDNA, synthetic oligonucleotides, and the like, or bindingproteins such as antibodies, that specifically interact with expressionproducts of individual components of the candidate library are affixedto the chip in a logically ordered manner, i.e., in an array. Inaddition, any molecule with a specific affinity for either the sense oranti-sense sequence of the marker nucleotide sequence (depending on thedesign of the sample labeling), can be fixed to the array surfacewithout loss of specific affinity for the marker and can be obtained andproduced for array production, for example, proteins that specificallyrecognize the specific nucleic acid sequence of the marker, ribozymes,peptide nucleic acids (PNA), or other chemicals or molecules withspecific affinity.

Detailed discussion of methods for linking nucleic acids and proteins toa chip substrate, are found in, e.g., U.S. Pat. No. 5,143,854 “LARGESCALE PHOTOLITHOGRAPHIC SOLID PHASE SYNTHESIS OF POLYPEPTIDES ANDRECEPTOR BINDING SCREENING THEREOF” to Pirrung et al., issued, Sep. 1,1992; U.S. Pat. No. 5,837,832 “ARRAYS OF NUCLEIC ACID PROBES ONBIOLOGICAL CHIPS” to Chee et al., issued Nov. 17, 1998; U.S. Pat. No.6,087,112 “ARRAYS WITH MODIFIED OLIGONUCLEOTIDE AND POLYNUCLEOTIDECOMPOSITIONS” to Dale, issued Jul. 11, 2000; U.S. Pat. No. 5,215,882“METHOD OF IMMOBILIZING NUCLEIC ACID ON A SOLID SUBSTRATE FOR USE INNUCLEIC ACID HYBRIDIZATION ASSAYS” to Bahl et al., issued Jun. 1, 1993;U.S. Pat. No. 5,707,807 “MOLECULAR INDEXING FOR EXPRESSED GENE ANALYSIS”to Kato, issued Jan. 13, 1998; U.S. Pat. No. 5,807,522 “METHODS FORFABRICATING MICROARRAYS OF BIOLOGICAL SAMPLES” to Brown et al., issuedSep. 15, 1998; U.S. Pat. No. 5,958,342 “JET DROPLET DEVICE” to Gamble etal., issued Sep. 28, 1999; U.S. Pat. No. 5,994,076 “METHODS OF ASSAYINGDIFFERENTIAL EXPRESSION” to Chenchik et al., issued Nov. 30, 1999; U.S.Pat. No. 6,004,755 “QUANTITATIVE MICROARRAY HYBRIDIZATION ASSAYS” toWang, issued Dec. 21, 1999; U.S. Pat. No. 6,048,695 “CHEMICALLY MODIFIEDNUCLEIC ACIDS AND METHOD FOR COUPLING NUCLEIC ACIDS TO SOLID SUPPORT” toBradley et al., issued Apr. 11, 2000; U.S. Pat. No. 6,060,240 “METHODSFOR MEASURING RELATIVE AMOUNTS OF NUCLEIC ACIDS IN A COMPLEX MIXTURE ANDRETRIEVAL OF SPECIFIC SEQUENCES THEREFROM” to Kamb et al., issued May 9,2000; U.S. Pat. No. 6,090,556 “METHOD FOR QUANTITATIVELY DETERMINING THEEXPRESSION OF A GENE” to Kato, issued Jul. 18, 2000; and U.S. Pat. No.6,040,138 “EXPRESSION MONITORING BY HYBRIDIZATION TO HIGH DENSITYOLIGONUCLEOTIDE ARRAYS” to Lockhart et al., issued Mar. 21, 2000 each ofwhich are hereby incorporated by reference in their entirety.

For example, cDNA inserts corresponding to candidate nucleotidesequences, in a standard TA cloning vector are amplified by a polymerasechain reaction for approximately 30-40 cycles. The amplified PCRproducts are then arrayed onto a glass support by any of a variety ofwell known techniques, e.g., the VSLIPS™ technology described in U.S.Pat. No. 5,143,854. RNA, or cDNA corresponding to RNA, isolated from asubject sample of leukocytes is labeled, e.g., with a fluorescent tag,and a solution containing the RNA (or cDNA) is incubated underconditions favorable for hybridization, with the “probe” chip. Followingincubation, and washing to eliminate non-specific hybridization, thelabeled nucleic acid bound to the chip is detected qualitatively orquantitatively, and the resulting expression profile for thecorresponding candidate nucleotide sequences is recorded. It isappreciated that the probe used for diagnostic purposes may be identicalto the probe used during diagnostic nucleotide sequence discovery andvalidation. Alternatively, the probe sequence may be different than thesequence used in diagnostic nucleotide sequence discovery andvalidation. Multiple cDNAs from a nucleotide sequence that arenon-overlapping or partially overlapping may also be used.

In another approach, oligonucleotides corresponding to members of ancandidate nucleotide library are synthesized and spotted onto an array.Alternatively, oligonucleotides are synthesized onto the array usingmethods known in the art, e.g. Hughes, et al. supra. The oligonucleotideis designed to be complementary to any portion of the candidatenucleotide sequence. In addition, in the context of expression analysisfor, e.g. diagnostic use of diagnostic nucleotide sets, anoligonucleotide can be designed to exhibit particular hybridizationcharacteristics, or to exhibit a particular specificity and/orsensitivity, as further described below.

Hybridization signal may be amplified using methods known in the art,and as described herein, for example use of the Clontech kit (GlassFluorescent Labeling Kit), Stratagene kit (Fairplay Microarray LabelingKit), the Micromax kit (New England Nuclear, Inc.), the Genisphere kit(3DNA Submicro), linear amplification, e.g. as described in U.S. Pat.No. 6,132,997 or described in Hughes, T R, et al., Nature Biotechnology,19:343-347 (2001) and/or Westin et al. Nat Biotech. 18:199-204.

Alternatively, fluorescently labeled cDNA are hybridized directly to themicroarray using methods known in the art. For example, labeled cDNA aregenerated by reverse transcription using Cy3- and Cy5-conjugateddeoxynucleotides, and the reaction products purified using standardmethods. It is appreciated that the methods for signal amplification ofexpression data useful for identifying diagnostic nucleotide sets arealso useful for amplification of expression data for diagnosticpurposes.

Microarray expression may be detected by scanning the microarray with avariety of laser or CCD-based scanners, and extracting features withnumerous software packages, for example, Imagene (Biodiscovery), FeatureExtraction (Agilent), Scanalyze (Eisen, M. 1999. SCANALYZE User Manual;Stanford Univ., Stanford, Calif. Ver 2.32.), GenePix (Axon Instruments).

In another approach, hybridization to microelectric arrays is performed,e.g. as described in Umek et al (2001) J Mol Diagn. 3:74-84. An affinityprobe, e.g. DNA, is deposited on a metal surface. The metal surfaceunderlying each probe is connected to a metal wire and electrical signaldetection system. Unlabelled RNA or cDNA is hybridized to the array, oralternatively, RNA or cDNA sample is amplified before hybridization,e.g. by PCR. Specific hybridization of sample RNA or cDNA results ingeneration of an electrical signal, which is transmitted to a detector.See Westin (2000) Nat Biotech. 18:199-204 (describing anchored multiplexamplification of a microelectronic chip array); Edman (1997) NAR25:4907-14; Vignali (2000) J Immunol Methods 243:243-55.

In another approach, a microfluidics chip is used for RNA samplepreparation and analysis. This approach increases efficiency becausesample preparation and analysis are streamlined. Briefly, microfluidicsmay be used to sort specific leukocyte sub-populations prior to RNApreparation and analysis. Microfluidics chips are also useful for, e.g.,RNA preparation, and reactions involving RNA (reverse transcription,RT-PCR). Briefly, a small volume of whole, anti-coagulated blood isloaded onto a microfluidics chip, for example chips available fromCaliper (Mountain View, Calif.) or Nanogen (San Diego, Calif.) Amicrofluidics chip may contain channels and reservoirs in which cellsare moved and reactions are performed. Mechanical, electrical, magnetic,gravitational, centrifugal or other forces are used to move the cellsand to expose them to reagents. For example, cells of whole blood aremoved into a chamber containing hypotonic saline, which results inselective lysis of red blood cells after a 20-minute incubation. Next,the remaining cells (leukocytes) are moved into a wash chamber andfinally, moved into a chamber containing a lysis buffer such asguanidine isothyocyanate. The leukocyte cell lysate is further processedfor RNA isolation in the chip, or is then removed for furtherprocessing, for example, RNA extraction by standard methods.Alternatively, the microfluidics chip is a circular disk containingficoll or another density reagent. The blood sample is injected into thecenter of the disc, the disc is rotated at a speed that generates acentrifugal force appropriate for density gradient separation ofmononuclear cells, and the separated mononuclear cells are thenharvested for further analysis or processing.

It is understood that the methods of expression evaluation, above,although discussed in the context of discovery of diagnostic nucleotidesets, are also applicable for expression evaluation when usingdiagnostic nucleotide sets for, e.g. diagnosis of diseases, as furtherdiscussed below.

Evaluation of Expression Patterns

Expression patterns can be evaluated by qualitative and/or quantitativemeasures. Certain of the above described techniques for evaluating geneexpression (as RNA or protein products) yield data that arepredominantly qualitative in nature. That is, the methods detectdifferences in expression that classify expression into distinct modeswithout providing significant information regarding quantitative aspectsof expression. For example, a technique can be described as aqualitative technique if it detects the presence or absence ofexpression of an candidate nucleotide sequence, i.e., an on/off patternof expression. Alternatively, a qualitative technique measures thepresence (and/or absence) of different alleles, or variants, of a geneproduct.

In contrast, some methods provide data that characterizes expression ina quantitative manner. That is, the methods relate expression on anumerical scale, e.g., a scale of 0-5, a scale of 1-10, a scale of+−+++, from grade 1 to grade 5, a grade from a to z, or the like. Itwill be understood that the numerical, and symbolic examples providedare arbitrary, and that any graduated scale (or any symbolicrepresentation of a graduated scale) can be employed in the context ofthe present invention to describe quantitative differences in nucleotidesequence expression. Typically, such methods yield informationcorresponding to a relative increase or decrease in expression.

Any method that yields either quantitative or qualitative expressiondata is suitable for evaluating expression of candidate nucleotidesequence in a subject sample of leukocytes. In some cases, e.g., whenmultiple methods are employed to determine expression patterns for aplurality of candidate nucleotide sequences, the recovered data, e.g.,the expression profile, for the nucleotide sequences is a combination ofquantitative and qualitative data.

In some applications, expression of the plurality of candidatenucleotide sequences is evaluated sequentially. This is typically thecase for methods that can be characterized as low- tomoderate-throughput. In contrast, as the throughput of the elected assayincreases, expression for the plurality of candidate nucleotidesequences in a sample or multiple samples of leukocytes, is assayedsimultaneously. Again, the methods (and throughput) are largelydetermined by the individual practitioner, although, typically, it ispreferable to employ methods that permit rapid, e.g. automated orpartially automated, preparation and detection, on a scale that istime-efficient and cost-effective.

It is understood that the preceding discussion, while directed at theassessment of expression of the members of candidate libraries, is alsoapplies to the assessment of the expression of members of diagnosticnucleotide sets, as further discussed below.

Genotyping

In addition to, or in conjunction with the correlation of expressionprofiles and clinical data, it is often desirable to correlateexpression patterns with the subject's genotype at one or more geneticloci. The selected loci can be, for example, chromosomal locicorresponding to one or more member of the candidate library,polymorphic alleles for marker loci, or alternative disease related loci(not contributing to the candidate library) known to be, or putativelyassociated with, a disease (or disease criterion). Indeed, it will beappreciated, that where a (polymorphic) allele at a locus is linked to adisease (or to a predisposition to a disease), the presence of theallele can itself be a disease criterion.

Numerous well known methods exist for evaluating the genotype of anindividual, including southern analysis, restriction fragment lengthpolymorphism (RFLP) analysis, polymerase chain reaction (PCR),amplification length polymorphism (AFLP) analysis, single strandedconformation polymorphism (SSCP) analysis, single nucleotidepolymorphism (SNP) analysis (e.g., via PCR, Taqman or molecularbeacons), among many other useful methods. Many such procedures arereadily adaptable to high throughput and/or automated (orsemi-automated) sample preparation and analysis methods. Most, can beperformed on nucleic acid samples recovered via simple procedures fromthe same sample of leukocytes as yielded the material for expressionprofiling. Exemplary techniques are described in, e.g., Sambrook, andAusubel, supra.

Identification of the Diagnostic Nucleotide Sets of the Invention

Identification of diagnostic nucleotide sets and disease specific targetnucleotide sequence proceeds by correlating the leukocyte expressionprofiles with data regarding the subject's health status to produce adata set designated a “molecular signature.” Examples of data regardinga patient's health status, also termed “disease criteria(ion)”, isdescribed below and in the Section titled “selected diseases,” below.Methods useful for correlation analysis are further described elsewherein the specification.

Generally, relevant data regarding the subject's health status includesretrospective or prospective health data, e.g., in the form of thesubject's medical history, as provided by the subject, physician orthird party, such as, medical diagnoses, laboratory test results,diagnostic test results, clinical events, or medication lists, asfurther described below. Such data may include information regarding apatient's response to treatment and/or a particular medication and dataregarding the presence of previously characterized “risk factors.” Forexample, cigarette smoking and obesity are previously identified riskfactors for heart disease. Further examples of health statusinformation, including diseases and disease criteria, is described inthe section titled Selected diseases, below.

Typically, the data describes prior events and evaluations (i.e.,retrospective data). However, it is envisioned that data collectedsubsequent to the sampling (i.e., prospective data) can also becorrelated with the expression profile. The tissue sampled, e.g.,peripheral blood, bronchial lavage, etc., can be obtained at one or moremultiple time points and subject data is considered retrospective orprospective with respect to the time of sample procurement.

Data collected at multiple time points, called “longitudinal data”, isoften useful, and thus, the invention encompasses the analysis ofpatient data collected from the same patient at different time points.Analysis of paired samples, such as samples from a patient at differenttime, allows identification of differences that are specifically relatedto the disease state since the genetic variability specific to thepatient is controlled for by the comparison. Additionally, othervariables that exist between patients may be controlled for in this way,for example, the presence or absence of inflammatory diseases (e.g.,rheumatoid arthritis) the use of medications that may effect leukocytegene expression, the presence or absence of co-morbid conditions, etc.Methods for analysis of paired samples are further described below.Moreover, the analysis of a pattern of expression profiles (generated bycollecting multiple expression profiles) provides information relatingto changes in expression level over time, and may permit thedetermination of a rate of change, a trajectory, or an expression curve.Two longitudinal samples may provide information on the change inexpression of a gene over time, while three longitudinal samples may benecessary to determine the “trajectory” of expression of a gene. Suchinformation may be relevant to the diagnosis of a disease. For example,the expression of a gene may vary from individual to individual, but aclinical event, for example, a heart attack, may cause the level ofexpression to double in each patient. In this example, clinicallyinteresting information is gleaned from the change in expression level,as opposed to the absolute level of expression in each individual.

When a single patient sample is obtained, it may still be desirable tocompare the expression profile of that sample to some referenceexpression profile. In this case, one can determine the change ofexpression between the patient's sample and a reference expressionprofile that is appropriate for that patient and the medical conditionin question. For example, a reference expression profile can bedetermined for all patients without the disease criterion in questionwho have similar characteristics, such as age, sex, race, diagnoses etc.

Generally, small sample sizes of 20-100 samples are used to identify adiagnostic nucleotide set. Larger sample sizes are generally necessaryto validate the diagnostic nucleotide set for use in large and variedpatient populations, as further described below. For example, extensionof gene expression correlations to varied ethnic groups, demographicgroups, nations, peoples or races may require expression correlationexperiments on the population of interest.

Expression Reference Standards

Expression profiles derived from a patient (i.e., subjects diagnosedwith, or exhibiting symptoms of, or exhibiting a disease criterion, orunder a doctor's care for a disease) sample are compared to a control orstandard expression RNA to facilitate comparison of expression profiles(e.g. of a set of candidate nucleotide sequences) from a group ofpatients relative to each other (i.e., from one patient in the group toother patients in the group, or to patients in another group).

The reference RNA used should have desirable features of low cost andsimplicity of production on a large scale. Additionally, the referenceRNA should contain measurable amounts of as many of the genes of thecandidate library as possible.

For example, in one approach to identifying diagnostic nucleotide sets,expression profiles derived from patient samples are compared to aexpression reference “standard.” Standard expression reference can be,for example, RNA derived from resting cultured leukocytes orcommercially available reference RNA, such as Universal reference RNAfrom Stratagene. See Nature, V406, 8-17-00, p. 747-752. Use of anexpression reference standard is particularly useful when the expressionof large numbers of nucleotide sequences is assayed, e.g. in an array,and in certain other applications, e.g. qualitative PCR, RT-PCR, etc.,where it is desirable to compare a sample profile to a standard profile,and/or when large numbers of expression profiles, e.g. a patientpopulation, are to be compared. Generally, an expression referencestandard should be available in large quantities, should be a goodsubstrate for amplification and labeling reactions, and should becapable of detecting a large percentage of candidate nucleic acids usingsuitable expression profiling technology.

Alternatively, or in addition, the expression profile derived from apatient sample is compared with the expression of an internal referencecontrol gene, for example, β-actin or CD4. The relative expression ofthe profiled genes and the internal reference control gene (from thesame individual) is obtained. An internal reference control may also beused with a reference RNA. For example, an expression profile for “gene1” and the gene encoding CD4 can be determined in a patient sample andin a reference RNA. The expression of each gene can be expressed as the“relative” ratio of expression the gene in the patient sample comparedwith expression of the gene in the reference RNA. The expression ratio(sample/reference) for gene 1 may be divided by the expression rationfor CD4 (sample/reference) and thus the relative expression of gene 1 toCD4 is obtained.

The invention also provides a buffy coat control RNA useful forexpression profiling, and a method of using control RNA produced from apopulation of buffy coat cells, the white blood cell layer derived fromthe centrifugation of whole blood. Buffy coat contains all white bloodcells, including granulocytes, mononuclear cells and platelets. Theinvention also provides a method of preparing control RNA from buffycoat cells for use in expression profile analysis of leukocytes. Buffycoat fractions are obtained, e.g. from a blood bank or directly fromindividuals, preferably from a large number of individuals such thatbias from individual samples is avoided and so that the RNA samplerepresents an average expression of a healthy population. Buffy coatfractions from about 50 or about 100, or more individuals are preferred.10 ml buffy coat from each individual is used. Buffy coat samples aretreated with an erthythrocyte lysis buffer, so that erthythrocytes areselectively removed. The leukocytes of the buffy coat layer arecollected by centrifugation. Alternatively, the buffy cell sample can befurther enriched for a particular leukocyte sub-populations, e.g.mononuclear cells, T-lymphocytes, etc. To enrich for mononuclear cells,the buffy cell pellet, above, is diluted in PBS (phosphate bufferedsaline) and loaded onto a non-polystyrene tube containing a polysucroseand sodium diatrizoate solution adjusted to a density of 1.077+/−0.001g/ml. To enrich for T-lymphocytes, 45 ml of whole blood is treated withRosetteSep (Stem Cell Technologies), and incubated at room temperaturefor 20 minutes. The mixture is diluted with an equal volume of PBS plus2% FBS and mixed by inversion. 30 ml of diluted mixture is layered ontop of 15 ml DML medium (Stem Cell Technologies). The tube iscentrifuged at 1200×g, and the enriched cell layer at the plasma:mediuminterface is removed, washed with PBS+2% FBS, and cells collected bycentrifugation at 1200×g. The cell pellet is treated with 5 ml oferythrocyte lysis buffer (EL buffer, Qiagen) for 10 minutes on ice, andenriched T-lymphocytes are collected by centrifugation.

In addition or alternatively, the buffy cells (whole buffy coat orsub-population, e.g. mononuclear fraction) can be cultured in vitro andsubjected to stimulation with cytokines or activating chemicals such asphorbol esters or ionomycin. Such stimuli may increase expression ofnucleotide sequences that are expressed in activated immune cells andmight be of interest for leukocyte expression profiling experiments.

Following sub-population selection and/or further treatment, e.g.stimulation as described above, RNA is prepared using standard methods.For example, cells are pelleted and lysed with a phenol/guanidiniumthiocyanate and RNA is prepared. RNA can also be isolated using a silicagel-based purification column or the column method can be used on RNAisolated by the phenol/guanidinium thiocyanate method. RNA fromindividual buffy coat samples can be pooled during this process, so thatthe resulting reference RNA represents the RNA of many individuals andindividual bias is minimized or eliminated. In addition, a new batch ofbuffy coat reference RNA can be directly compared to the last batch toensure similar expression pattern from one batch to another, usingmethods of collecting and comparing expression profiles describedabove/below. One or more expression reference controls are used in anexperiment. For example, RNA derived from one or more of the followingsources can be used as controls for an experiment: stimulated orunstimulated whole buffy coat, stimulated or unstimulated peripheralmononuclear cells, or stimulated or unstimulated T-lymphocytes.

Alternatively, the expression reference standard can be derived from anysubject or class of subjects including healthy subjects or subjectsdiagnosed with the same or a different disease or disease criterion.Expression profiles from subjects in two distinct classes are comparedto determine which subset of nucleotide sequences in the candidatelibrary best distinguish between the two subject classes, as furtherdiscussed below. It will be appreciated that in the present context, theterm “distinct classes” is relevant to at least one distinguishablecriterion relevant to a disease of interest, a “disease criterion.” Theclasses can, of course, demonstrate significant overlap (or identity)with respect to other disease criteria, or with respect to diseasediagnoses, prognoses, or the like. The mode of discovery involves, e.g.,comparing the molecular signature of different subject classes to eachother (such as patient to control, patients with a first diagnosis topatients with a second diagnosis, etc.) or by comparing the molecularsignatures of a single individual taken at different time points. Theinvention can be applied to a broad range of diseases, disease criteria,conditions and other clinical and/or epidemiological questions, asfurther discussed above/below.

It is appreciated that while the present discussion pertains to the useof expression reference controls while identifying diagnostic nucleotidesets, expression reference controls are also useful during use ofdiagnostic nucleotide sets, e.g. use of a diagnostic nucleotide set fordiagnosis of a disease, as further described below.

Analysis of Expression Profiles

In order to facilitate ready access, e.g., for comparison, review,recovery, and/or modification, the molecular signatures/expressionprofiles are typically recorded in a database. Most typically, thedatabase is a relational database accessible by a computational device,although other formats, e.g., manually accessible indexed files ofexpression profiles as photographs, analogue or digital imagingreadouts, spreadsheets, etc. can be used. Further details regardingpreferred embodiments are provided below. Regardless of whether theexpression patterns initially recorded are analog or digital in natureand/or whether they represent quantitative or qualitative differences inexpression, the expression patterns, expression profiles (collectiveexpression patterns), and molecular signatures (correlated expressionpatterns) are stored digitally and accessed via a database. Typically,the database is compiled and maintained at a central facility, withaccess being available locally and/or remotely.

As additional samples are obtained, and their expression profilesdetermined and correlated with relevant subject data, the ensuingmolecular signatures are likewise recorded in the database. However,rather than each subsequent addition being added in an essentiallypassive manner in which the data from one sample has little relation todata from a second (prior or subsequent) sample, the algorithmsoptionally additionally query additional samples against the existingdatabase to further refine the association between a molecular signatureand disease criterion. Furthermore, the data set comprising the one (ormore) molecular signatures is optionally queried against an expandingset of additional or other disease criteria. The use of the database inintegrated systems and web embodiments is further described below.

Analysis of Expression Profile Data from Arrays

Expression data is analyzed using methods well known in the art,including the software packages Imagene (Biodiscovery, Marina del Rey,Calif.), Feature Extraction Software (Agilent, Palo Alto, Calif.), andScanalyze (Stanford University). In the discussion that follows, a“feature” refers to an individual spot of DNA on an array. Each gene maybe represented by more than one feature. For example, hybridizedmicroarrays are scanned and analyzed on an Axon Instruments scannerusing GenePix 3.0 software (Axon Instruments, Union City, Calif.). Thedata extracted by GenePix is used for all downstream quality control andexpression evaluation. The data is derived as follows. The data for allfeatures flagged as “not found” by the software is removed from thedataset for individual hybridizations. The “not found” flag by GenePixindicates that the software was unable to discriminate the feature fromthe background. Each feature is examined to determine the value of itssignal. The median pixel intensity of the background (B_(n)) issubtracted from the median pixel intensity of the feature (F_(n)) toproduce the background-subtracted signal (hereinafter, “BGSS”). The BGSSis divided by the standard deviation of the background pixels to providethe signal-to-noise ratio (hereinafter, “S/N”). Features with a S/N ofthree or greater in both the Cy3 channel (corresponding to the sampleRNA) and Cy5 channel (corresponding to the reference RNA) are used forfurther analysis (hereinafter denoted “useable features”).Alternatively, different S/Ns are used for selecting expression data foran analysis. For example, only expression data with signal to noiseratios >3 might be used in an analysis. Alternatively, features with S/Nvalues <3 may be flagged as such and included in the analysis. Suchflagged data sets include more values and may allow one to discoverexpression markers that would be missed otherwise. However, such datasets may have a higher variability than filtered data, which maydecrease significance of findings or performance of correlationstatistics.

For each usable feature (i), the expression level (e) is expressed asthe logarithm of the ratio (R) of the Background Subtracted Signal(hereinafter “BGSS”) for the Cy3 (sample RNA) channel divided by theBGSS for the Cy5 channel (reference RNA) This “log ratio” value is usedfor comparison to other experiments.

$\begin{matrix}{R_{i} = \frac{{BGSS}_{sample}}{{BGSS}_{reference}}} & (0.1) \\{e_{i} = {\log\; r_{1}}} & (0.2)\end{matrix}$

Variation in signal across hybridizations may be caused by a number offactors affecting hybridization, DNA spotting, wash conditions, andlabeling efficiency.

A single reference RNA may be used with all of the experimental RNAs,permitting multiple comparisons in addition to individual comparisons.By comparing sample RNAs to the same reference, the gene expressionlevels from each sample are compared across arrays, permitting the useof a consistent denominator for our experimental ratios.

Alternative methods of analyzing the data may involve 1) using thesample channel without normalization by the reference channel, 2) usingan intensity-dependent normalization based on the reference whichprovides a greater correction when the signal in the reference channelis large, 3) using the data without background subtraction orsubtracting an empirically derived function of the background intensityrather than the background itself.

Scaling

The data may be scaled (normalized) to control for labeling andhybridization variability within the experiment, using methods known inthe art. Scaling is desirable because it facilitates the comparison ofdata between different experiments, patients, etc. Generally the BGSSare scaled to a factor such as the median, the mean, the trimmed mean,and percentile. Additional methods of scaling include: to scale between0 and 1, to subtract the mean, or to subtract the median.

Scaling is also performed by comparison to expression patterns obtainedusing a common reference RNA, as described in greater detail above. Aswith other scaling methods, the reference RNA facilitates multiplecomparisons of the expression data, e.g., between patients, betweensamples, etc. Use of a reference RNA provides a consistent denominatorfor experimental ratios.

In addition to the use of a reference RNA, individual expression levelsmay be adjusted to correct for differences in labeling efficiencybetween different hybridization experiments, allowing direct comparisonbetween experiments with different overall signal intensities, forexample. A scaling factor (α) may be used to adjust individualexpression levels as follows. The median of the scaling factor (α), forexample, BGSS, is determined for the set of all features with a S/Ngreater than three. Next, the BGSS_(i) (the BGSS for each feature “i”)is divided by the median for all features (α), generating a scaledratio. The scaled ration is used to determine the expression value forthe feature (e_(i)), or the log ratio.

$\begin{matrix}{S_{i} = \frac{{BGSS}_{i}}{a}} & (0.3) \\{e_{i} = {\log\left( \frac{{Cy3S}_{1}}{{Cy5S}_{i}} \right)}} & (0.4)\end{matrix}$

In addition, or alternatively, control features are used to normalizethe data for labeling and hybridization variability within theexperiment. Control feature may be cDNA for genes from the plant,Arabidopsis thaliana, that are included when spotting the mini-array.Equal amounts of RNA complementary to control cDNAs are added to each ofthe samples before they were labeled. Using the signal from thesecontrol genes, a normalization constant (L) is determined according tothe following formula:

$L_{j} = \frac{\frac{\sum\limits_{i = 1}^{N}\;{BGSS}_{j,i}}{N}}{\frac{\sum\limits_{j = 1}^{K}\;\frac{\sum\limits_{i = 1}^{N}\;{BGSS}_{j,i}}{N}}{K}}$where BGSS_(i) is the signal for a specific feature, N is the number ofA. thaliana control features, K is the number of hybridizations, andL_(j) is the normalization constant for each individual hybridization.

Using the formula above, the mean for all control features of aparticular hybridization and dye (e.g., Cy3) is calculated. The controlfeature means for all Cy3 hybridizations are averaged, and the controlfeature mean in one hybridization divided by the average of allhybridizations to generate a normalization constant for that particularCy3 hybridization (L_(j)), which is used as a in equation (0.3). Thesame normalization steps may be performed for Cy3 and Cy5 values.

An alternative scaling method can also be used. The log of the ratio ofGreen/Red is determined for all features. The median log ratio value forall features is determined. The feature values are then scaled using thefollowing formula:Log_Scaled_Feature_Ratio=Log_Feature_Ratio−Median_Log_Ratio.

Many additional methods for normalization exist and can be applied tothe data. In one method, the average ratio of Cy3 BGSS/Cy5 BGSS isdetermined for all features on an array. This ratio is then scaled tosome arbitrary number, such as 1 or some other number. The ratio foreach probe is then multiplied by the scaling factor required to bringthe average ratio to the chosen level. This is performed for each arrayin an analysis. Alternatively, the ratios are normalized to the averageratio across all arrays in an analysis. Other methods of normalizationinclude forcing the distribution of signal strengths of the variousarrays into greater agreement by transforming them to match certainpoints (quartiles, or deciles, etc.) in a standard distribution, or inthe most extreme case using the rank of the signal of eacholigonucleotide relative to the other oligonucleotides on the array.

If multiple features are used per gene sequence or oligonucleotide,these repeats can be used to derive an average expression value for eachgene. If some of the replicate features are of poor quality and don'tmeet requirements for analysis, the remaining features can be used torepresent the gene or gene sequence.

Correlation Analysis

Correlation analysis is performed to determine which array probes haveexpression behavior that best distinguishes or serves as markers forrelevant groups of samples representing a particular clinical condition.Correlation analysis, or comparison among samples representing differentdisease criteria (e.g., clinical conditions), is performed usingstandard statistical methods. Numerous algorithms are useful forcorrelation analysis of expression data, and the selection of algorithmsdepends in part on the data analysis to be performed. For example,algorithms can be used to identify the single most informative gene withexpression behavior that reliably classifies samples, or to identify allthe genes useful to classify samples. Alternatively, algorithms can beapplied that determine which set of 2 or more genes have collectiveexpression behavior that accurately classifies samples. The use ofmultiple expression markers for diagnostics may overcome the variabilityin expression of a gene between individuals, or overcome the variabilityintrinsic to the assay. Multiple expression markers may includeredundant markers (surrogates), in that two or more genes or probes mayprovide the same information with respect to diagnosis. This may occur,for example, when two or more genes or gene probes are coordinatelyexpressed. For diagnostic application, it may be appropriate to utilizea gene and one or more of its surrogates in the assay. This redundancymay overcome failures (technical or biological) of a single marker todistinguish samples. Alternatively, one or more surrogates may haveproperties that make them more suitable for assay development, such as ahigher baseline level of expression, better cell specificity, a higherfold change between sample groups or more specific sequence for thedesign of PCR primers or complimentary probes. It will be appreciatedthat while the discussion above pertains to the analysis of RNAexpression profiles the discussion is equally applicable to the analysisof profiles of proteins or other molecular markers.

Prior to analysis, expression profile data may be formatted or preparedfor analysis using methods known in the art. For example, often the logratio of scaled expression data for every array probe is calculatedusing the following formula:

log (Cy 3 BGSS/Cy5 BGSS), where Cy 3 signal corresponds to theexpression of the gene in the clinical sample, and Cy5 signalcorresponds to expression of the gene in the reference RNA.

Data may be further filtered depending on the specific analysis to bedone as noted below. For example, filtering may be aimed at selectingonly samples with expression above a certain level, or probes withvariability above a certain level between sample sets.

The following non-limiting discussion consider several statisticalmethods known in the art. Briefly, the t-test and ANOVA are used toidentify single genes with expression differences between or amongpopulations, respectively. Multivariate methods are used to identify aset of two or more genes for which expression discriminates between twodisease states more specifically than expression of any single gene.

t-test

The simplest measure of a difference between two groups is the Student'st test. See, e.g., Welsh et al. (2001) Proc Natl Acad Sci USA 98:1176-81(demonstrating the use of an unpaired Student's t-test for the discoveryof differential gene expression in ovarian cancer samples and controltissue samples). The t-test assumes equal variance and normallydistributed data. This test identifies the probability that there is adifference in expression of a single gene between two groups of samples.The number of samples within each group that is required to achievestatistical significance is dependent upon the variation among thesamples within each group. The standard formula for a t-test is:

$\begin{matrix}{{{t\left( e_{i} \right)} = \frac{{\overset{\_}{e}}_{i,c} - {\overset{\_}{e}}_{i,t}}{\sqrt{\left( {s_{i,c}^{2}/n_{c}} \right) + \left( {s_{i,t}^{2}/n_{t}} \right)}}},} & (0.5)\end{matrix}$where ē_(i) is the difference between the mean expression level of genei in groups c and t, s_(i,c) is the variance of gene x in group c ands_(i,t) is the variance of gene x in group t. n_(c) and n_(i) are thenumbers of samples in groups c and t.

The combination of the t statistic and the degrees of freedom[min(n_(t), n_(c))−1] provides a p value, the probability of rejectingthe null hypothesis. A p-value of ≦0.01, signifying a 99 percentprobability the mean expression levels are different between the twogroups (a 1% chance that the mean expression levels are in fact notdifferent and that the observed difference occurred by statisticalchance), is often considered acceptable.

When performing tests on a large scale, for example, on a large datasetof about 8000 genes, a correction factor must be included to adjust forthe number of individual tests being performed. The most common andsimplest correction is the Bonferroni correction for multiple tests,which divides the p-value by the number of tests run. Using this test onan 8000 member dataset indicates that a p value of ≦0.00000125 isrequired to identify genes that are likely to be truly different betweenthe two test conditions.

Significance Analysis for Microarrays (SAM)

Significance analysis for microarrays (SAM) (Tusher 2001) is a methodthrough which genes with a correlation between their expression valuesand the response vector are statistically discovered and assigned astatistical significance. The ratio of false significant to significantgenes is the False Discovery Rate (FDR). This means that for eachthreshold there are a set of genes which are called significant, and theFDR gives a confidence level for this claim. If a gene is calleddifferentially expressed between 2 classes by SAM, with a FDR of 5%,there is a 95% chance that the gene is actually differentially expressedbetween the classes. SAM takes into account the variability and largenumber of variables of microarrays. SAM will identify genes that aremost globally differentially expressed between the classes. Thus,important genes for identifying and classifying outlier samples orpatients may not be identified by SAM.

Non-Parametric Tests

Wilcoxon's signed ranks method is one example of a non-parametric testand is utilized for paired comparisons. See e.g., Sokal and Rohlf (1987)Introduction to Biostatistics 2^(nd) edition, WH Freeman, New York. Atleast 6 pairs are necessary to apply this statistic. This test is usefulfor analysis of paired expression data (for example, a set of patientswho have cardiac transplant biopsy on 2 occasions and have a grade 0 onone occasion and a grade 3A on another). The Fisher Exact Test with athreshold and the Mann-Whitney Test are other non-parametric tests thatmay be used.

ANOVA

Differences in gene expression across multiple related groups may beassessed using an Analysis of Variance (ANOVA), a method well known inthe art (Michelson and Schofield, 1996).

Multivariate Analysis

Many algorithms suitable for multivariate analysis are known in the art.Generally, a set of two or more genes for which expression discriminatesbetween two disease states more specifically than expression of anysingle gene is identified by searching through the possible combinationsof genes using a criterion for discrimination, for example theexpression of gene X must increase from normal 300 percent, while theexpression of genes Y and Z must decrease from normal by 75 percent.Ordinarily, the search starts with a single gene, then adds the nextbest fit at each step of the search. Alternatively, the search startswith all of the genes and genes that do not aid in the discriminationare eliminated step-wise.

Paired Samples

Paired samples, or samples collected at different time-points from thesame patient, are often useful, as described above. For example, use ofpaired samples permits the reduction of variation due to geneticvariation among individuals. In addition, the use of paired samples hasa statistical significance, in that data derived from paired samples canbe calculated in a different manner that recognizes the reducedvariability. For example, the formula for a t-test for paired samplesis:

$\begin{matrix}{{{t\left( e_{x} \right)} = \frac{{\overset{\_}{D}}_{\overset{\_}{e}x}}{\sqrt{\frac{{\sum\; D^{2}} - {\left( {\sum\limits^{\;}\; D} \right)^{2}/b}}{b - 1}}}},} & (0.5)\end{matrix}$where D is the difference between each set of paired samples and b isthe number of sample pairs. D is the mean of the differences between themembers of the pairs. In this test, only the differences between thepaired samples are considered, then grouped together (as opposed totaking all possible differences between groups, as would be the casewith an ordinary t-test). Additional statistical tests useful withpaired data, e.g., ANOVA and Wilcoxon's signed rank test, are discussedabove.

Diagnostic Classification

Once a discriminating set of genes is identified, the diagnosticclassifier (a mathematical function that assigns samples to diagnosticcategories based on expression data) is applied to unknown sampleexpression levels.

Methods that can be used for this analysis include the followingnon-limiting list:

CLEAVER is an algorithm used for classification of useful expressionprofile data. See Raychaudhuri et al. (2001) Trends Biotechnol19:189-193. CLEAVER uses positive training samples (e.g., expressionprofiles from samples known to be derived from a particular patient orsample diagnostic category, disease or disease criteria), negativetraining samples (e.g., expression profiles from samples known not to bederived from a particular patient or sample diagnostic category, diseaseor disease criteria) and test samples (e.g., expression profilesobtained from a patient), and determines whether the test samplecorrelates with the particular disease or disease criteria, or does notcorrelate with a particular disease or disease criteria. CLEAVER alsogenerates a list of the 20 most predictive genes for classification.

Artificial neural networks (hereinafter, “ANN”) can be used to recognizepatterns in complex data sets and can discover expression criteria thatclassify samples into more than 2 groups. The use of artificial neuralnetworks for discovery of gene expression diagnostics for cancers usingexpression data generated by oligonucleotide expression microarrays isdemonstrated by Khan et al. (2001) Nature Med. 7:673-9. Khan found that96 genes provided 0% error rate in classification of the tumors. Themost important of these genes for classification was then determined bymeasuring the sensitivity of the classification to a change inexpression of each gene. Hierarchical clustering using the 96 genesresults in correct grouping of the cancers into diagnostic categories.

Golub uses cDNA microarrays and a distinction calculation to identifygenes with expression behavior that distinguishes myeloid and lymphoidleukemias. See Golub et al. (1999) Science 286:531-7. Self organizingmaps were used for new class discovery. Cross validation was done with a“leave one out” analysis. 50 genes were identified as useful markers.This was reduced to as few as 10 genes with equivalent diagnosticaccuracy.

Hierarchical and non-hierarchical clustering methods are also useful foridentifying groups of genes that correlate with a subset of clinicalsamples such as with transplant rejection grade. Alizadeh usedhierarchical clustering as the primary tool to distinguish differenttypes of diffuse B-cell lymphomas based on gene expression profile data.See Alizadeh et al. (2000) Nature 403:503-11. Alizadeh used hierarchicalclustering as the primary tool to distinguish different types of diffuseB-cell lymphomas based on gene expression profile data. A cDNA arraycarrying 17856 probes was used for these experiments, 96 samples wereassessed on 128 arrays, and a set of 380 genes was identified as beinguseful for sample classification.

Perou demonstrates the use of hierarchical clustering for the molecularclassification of breast tumor samples based on expression profile data.See Perou et al. (2000) Nature 406:747-52. In this work, a cDNA arraycarrying 8102 gene probes was used. 1753 of these genes were found tohave high variation between breast tumors and were used for theanalysis.

Hastie describes the use of gene shaving for discovery of expressionmarkers. Hastie et al. (2000) Genome Biol. 1(2):RESEARCH 0003.1-0003.21.The gene shaving algorithm identifies sets of genes with similar orcoherent expression patterns, but large variation across conditions (RNAsamples, sample classes, patient classes). In this manner, genes with atight expression pattern within a transplant rejection grade, but alsowith high variability across rejection grades are grouped together. Thealgorithm takes advantage of both characteristics in one grouping step.For example, gene shaving can identify useful marker genes withco-regulated expression. Sets of useful marker genes can be reduced to asmaller set, with each gene providing some non-redundant value inclassification. This algorithm was used on the data set described inAlizadeh et al., supra, and the set of 380 informative gene markers wasreduced to 234.

Supervised harvesting of expression trees (Hastie 2001) identifies genesor clusters that best distinguish one class from all the others on thedata set. The method is used to identify the genes/clusters that canbest separate one class versus all the others for datasets that includetwo or more classes or all classes from each other. This algorithm canbe used for discovery or testing of a diagnostic gene set.

CART is a decision tree classification algorithm (Breiman 1984). Fromgene expression and or other data, CART can develop a decision tree forthe classification of samples. Each node on the decision tree involves aquery about the expression level of one or more genes or variables.Samples that are above the threshold go down one branch of the decisiontree and samples that are not go down the other branch. See example 4for further description of its use in classification analysis andexamples of its usefulness in discovering and implementing a diagnosticgene set. CART identifies surrogates for each splitter (genes that arethe next best substitute for a useful gene in classification.

Multiple Additive Regression Trees (Friedman, J H 1999, MART) is similarto CART in that it is a classification algorithm that builds decisiontrees to distinguish groups. MART builds numerous trees for anyclassification problem and the resulting model involves a combination ofthe multiple trees. MART can select variables as it build models andthus can be used on large data sets, such as those derived from an 8000gene microarray. Because MART uses a combination of many trees and doesnot take too much information from any one tree, it resists overtraining. MART identifies a set of genes and an algorithm for their useas a classifier.

A Nearest Shrunken Centroids Classifier can be applied to microarray orother data sets by the methods described by Tibshirani et al. 2002. Thisalgorithms also identified gene sets for classification and determinestheir 10 fold cross validation error rates for each class of samples.The algorithm determines the error rates for models of any size, fromone gene to all genes in the set. The error rates for either or bothsample classes can are minimized when a particular number of genes areused. When this gene number is determined, the algorithm associated withthe selected genes can be identified and employed as a classifier onprospective sample.

Once a set of genes and expression criteria for those genes have beenestablished for classification, cross validation is done. There are manyapproaches, including a 10 fold cross validation analysis in which 10%of the training samples are left out of the analysis and theclassification algorithm is built with the remaining 90%. The 10% arethen used as a test set for the algorithm. The process is repeated 10times with 10% of the samples being left out as a test set each time.Through this analysis, one can derive a cross validation error whichhelps estimate the robustness of the algorithm for use on prospective(test) samples.

Clinical data are gathered for every patient sample used for expressionanalysis. Clinical variables can be quantitative or non-quantitative. Aclinical variable that is quantitative can be used as a variable forsignificance or classification analysis. Non-quantitative clinicalvariables, such as the sex of the patient, can also be used in asignificance analysis or classification analysis with some statisticaltool. It is appreciated that the most useful diagnostic gene set for acondition may be optimal when considered along with one or morepredictive clinical variables. Clinical data can also be used assupervising vectors for a correlation analysis. That is to say that theclinical data associated with each sample can be used to divide thesamples into meaningful diagnostic categories for analysis. For example,samples can be divided into 2 or more groups based on the presence orabsence of some diagnostic criterion (a). In addition, clinical data canbe utilized to select patients for a correlation analysis or to excludethem based on some undesirable characteristic, such as an ongoinginfection, a medicine or some other issue. Clinical data can also beused to assess the pre-test probability of an outcome. For example,patients who are female are much more likely to be diagnosed as havingsystemic lupus erythematosis than patients who are male.

Once a set of genes are identified that classify samples with acceptableaccuracy. These genes are validated as a set using new samples that werenot used to discover the gene set. These samples can be taken fromfrozen archives from the discovery clinical study or can be taken fromnew patients prospectively. Validation using a “test set” of samples canbe done using expression profiling of the gene set with microarrays orusing real-time PCR for each gene on the test set samples.Alternatively, a different expression profiling technology can be used.

Immune Monitoring

Leukocyte gene expression can be used to monitor the immune system.Immune monitoring examines both the level of gene expression for a setof genes in a given cell type and for genes which are expressed in acell type selective manner gene expression monitoring will also detectthe presence or absence of new cell types, progenitor cells,differentiation of cells and the like. Gene expression patterns may beassociated with activation or the resting state of cells of the immunesystem that are responsible for or responsive to a disease state. Forexample, in the process of transplant rejection, cells of the immunesystem are activated by the presence of the foreign tissue. Genes andgene sets that monitor and diagnose this process are providing a measureof the level and type of activation of the immune system. Genes and genesets that are useful in monitoring the immune system may be useful fordiagnosis and monitoring of all diseases that involve the immune system.Some examples are transplant rejection, rheumatoid arthritis, lupus,inflammatory bowel diseases, multiple sclerosis, HIV/AIDS, and viral,bacterial and fungal infection. All disorders and diseases disclosedherein are contemplated. Genes and gene sets that monitor immuneactivation are useful for monitoring response to immunosuppressive drugtherapy, which is used to decrease immune activation. Genes are found tocorrelate with immune activation by correlation of expression patternsto the known presence of immune activation or quiescence in a sample asdetermined by some other test.

Selected Diseases

In principle, diagnostic nucleotide sets of the invention may bedeveloped and applied to essentially any disease, or disease criterion,as long as at least one subset of nucleotide sequences is differentiallyexpressed in samples derived from one or more individuals with a diseasecriteria or disease and one or more individuals without the diseasecriteria or disease, wherein the individual may be the same individualsampled at different points in time, or the individuals may be differentindividuals (or populations of individuals). For example, the subset ofnucleotide sequences may be differentially expressed in the sampledtissues of subjects with the disease or disease criterion (e.g., apatient with a disease or disease criteria) as compared to subjectswithout the disease or disease criterion (e.g., patients without adisease (control patients)). Alternatively, or in addition, the subsetof nucleotide sequence(s) may be differentially expressed in differentsamples taken from the same patient, e.g. at different points in time,at different disease stages, before and after a treatment, in thepresence or absence of a risk factor, etc.

Expression profiles corresponding to sets of nucleotide sequences thatcorrelate not with a diagnosis, but rather with a particular aspect of adisease can also be used to identify the diagnostic nucleotide sets anddisease specific target nucleotide sequences of the invention. Forexample, such an aspect, or disease criterion, can relate to a subject'smedical or family history, e.g., childhood illness, cause of death of aparent or other relative, prior surgery or other intervention,medications, symptoms (including onset and/or duration of symptoms),etc. Alternatively, the disease criterion can relate to a diagnosis,e.g., hypertension, diabetes, atherosclerosis, or prognosis (e.g.,prediction of future diagnoses, events or complications), e.g., acutemyocardial infarction, restenosis following angioplasty, reperfusioninjury, allograft rejection, rheumatoid arthritis or systemic lupuserythematosis disease activity or the like. In other cases, the diseasecriterion corresponds to a therapeutic outcome, e.g., transplantrejection, bypass surgery or response to a medication, restenosis afterstent implantation, collateral vessel growth due to therapeuticangiogenesis therapy, decreased angina due to revascularization,resolution of symptoms associated with a myriad of therapies, and thelike. Alternatively, the disease criteria corresponds with previouslyidentified or classic risk factors and may correspond to prognosis orfuture disease diagnosis. As indicated above, a disease criterion canalso correspond to genotype for one or more loci. Disease criteria(including patient data) may be collected (and compared) from the samepatient at different points in time, from different patients, betweenpatients with a disease (criterion) and patients representing a controlpopulation, etc. Longitudinal data, i.e., data collected at differenttime points from an individual (or group of individuals) may be used forcomparisons of samples obtained from an individual (group ofindividuals) at different points in time, to permit identification ofdifferences specifically related to the disease state, and to obtaininformation relating to the change in expression over time, including arate of change or trajectory of expression over time. The usefulness oflongitudinal data is further discussed in the section titled“Identification of diagnostic nucleotide sets of the invention”.

It is further understood that diagnostic nucleotide sets may bedeveloped for use in diagnosing conditions for which there is no presentmeans of diagnosis. For example, in rheumatoid arthritis, jointdestruction is often well under way before a patient experience symptomsof the condition. A diagnostic nucleotide set may be developed thatdiagnoses rheumatic joint destruction at an earlier stage than would bepossible using present means of diagnosis, which rely in part on thepresentation of symptoms by a patient. Diagnostic nucleotide sets mayalso be developed to replace or augment current diagnostic procedures.For example, the use of a diagnostic nucleotide set to diagnose cardiacallograft rejection may replace the current diagnostic test, a graftbiopsy.

It is understood that the following discussion of diseases is exemplaryand non-limiting, and further that the general criteria discussed above,e.g. use of family medical history, are generally applicable to thespecific diseases discussed below.

In addition to leukocytes, as described throughout, the general methodis applicable to nucleotide sequences that are differentially expressedin any subject tissue or cell type, by the collection and assessment ofsamples of that tissue or cell type. However, in many cases, collectionof such samples presents significant technical or medical problems giventhe current state of the art.

Organ Transplant Rejection and Success

A frequent complication of organ transplantation is recognition of thetransplanted organ as foreign by the immune system resulting inrejection. Diagnostic nucleotide sets can be identified and validatedfor monitoring organ transplant success, rejection and treatment.Medications currently exist that suppress the immune system, and therebydecrease the rate of and severity of rejection. However, these drugsalso suppress the physiologic immune responses, leaving the patientsusceptible to a wide variety of opportunistic infections and cancers.At present there is no easy, reliable way to diagnose transplantrejection. Organ biopsy is the preferred method, but this is expensive,painful and associated with significant risk and has inadequatesensitivity for focal rejection.

Diagnostic nucleotide sets of the present invention can be developed andvalidated for use as diagnostic tests for transplant rejection andsuccess. It is appreciated that the methods of identifying diagnosticnucleotide sets are applicable to any organ transplant population. Forexample, diagnostic nucleotide sets are developed for cardiac allograftrejection and success.

In some cases, disease criteria correspond to acute stage rejectiondiagnosis based on organ biopsy and graded using the InternationalSociety for Heart and Lung Transplantation (“ISHLT”) criteria. Thisgrading system classifies endomyocardial biopsies on the histologicallevel as Grade 0, 1A, 1B, 2, 3A, 3B, or 4. Grade 0 biopsies have noevidence of rejection, while each successive grade has increasedseverity of leukocyte infiltration and/or damage to the graft myocardialcells. It is appreciated that there is variability in the Gradingsystems between medical centers and pathologists and between repeatedreadings of the same pathologist at different times. When using thebiopsy grade as a disease criterion for leukocyte gene expressioncorrelation analysis, it may be desirable to have a single pathologistread all biopsy slides or have multiple pathologists read all slides todetermine the variability in this disease criterion. It is alsoappreciated that cardiac biopsy, in part due to variability, is not 100%sensitive or 100% specific for diagnosing acute rejection. When usingthe cardiac biopsy grade as a disease criterion for the discovery ofdiagnostic gene sets, it may be desirable to divide patient samples intodiagnostic categories based on the grades. Examples of such classes arethose patients with: Grade 0 vs. Grades 1A-4, Grade 0 vs. Grades 1B-4,Grade 0 vs. Grades 2-4, Grade 0-1 vs. Grade 2-4, Grade 0-1 vs. Grade3A-4, or Grade 0 vs. Grade 3A-4.

Other disease criteria correspond to the cardiac biopsy results andother criteria, such as the results of cardiac function testing byechocardiography, hemodynamics assessment by cardiac catheterization,CMV infection, weeks post transplant, medication regimen, demographicsand/or results of other diagnostic tests.

Other disease criteria correspond to information from the patient'smedical history and information regarding the organ donor.Alternatively, disease criteria include the presence or absence ofcytomegalovirus (CMV) infection, Epstein-Barr virus (EBV) infection,allograft dysfunction measured by physiological tests of cardiacfunction (e.g., hemodynamic measurements from catheterization orechocardiograph data), and symptoms of other infections. Alternatively,disease criteria correspond to therapeutic outcome, e.g. graft failure,re-transplantation, death, hospitalization, need for intravenousimmunosuppression, transplant vasculopathy, response toimmunosuppressive medications, etc. Disease criteria may furthercorrespond to a rejection episode of at least moderate histologic grade,which results in treatment of the patient with additionalcorticosteroids, anti-T cell antibodies, or total lymphoid irradiation;a rejection with histologic grade 2 or higher; a rejection withhistologic grade <2; the absence of histologic rejection and normal orunchanged allograft function (based on hemodynamic measurements fromcatheterization or on echocardiographic data); the presence of severeallograft dysfunction or worsening allograft dysfunction during thestudy period (based on hemodynamic measurements from catheterization oron echocardiographic data); documented CMV infection by culture,histology, or PCR, and at least one clinical sign or symptom ofinfection; specific graft biopsy rejection grades; rejection of mild tomoderate histologic severity prompting augmentation of the patient'schronic immunosuppressive regimen; rejection of mild to moderateseverity with allograft dysfunction prompting plasmaphoresis or adiagnosis of “humoral” rejection; infections other than CMV, especiallyinfection with Epstein Barr virus (EBV); lymphoproliferative disorder(also called post-transplant lymphoma); transplant vasculopathydiagnosed by increased intimal thickness on intravascular ultrasound(IVUS), angiography, or acute myocardial infarction; graft failure orretransplantation; and all cause mortality. Further specific examples ofclinical data useful as disease criteria are provided in Example 3.

In another example, diagnostic nucleotide sets are developed andvalidated for use in diagnosis and monitoring of kidney allograftrecipients. Disease criteria correspond to, e.g., results of biopsyanalysis for kidney allograft rejection, serum creatine level,creatinine clearance, radiological imaging results for the kidney andurinalysis results. Another disease criterion corresponds to the needfor hemodialysis, retransplantation, death or other renal replacementtherapy. Diagnostic nucleotide sets are developed and validated for usein diagnosis and treatment of bone marrow transplant and livertransplantation patients, respectively. Disease criteria for bone marrowtransplant correspond to the diagnosis and monitoring of graft rejectionand/or graft versus host disease, the recurrence of cancer,complications due to immunosuppression, hematologic abnormalities,infection, hospitalization and/or death. Disease criteria for livertransplant rejection include levels of serum markers for liver damageand liver function such as AST (aspartate aminotransferase), ALT(alanine aminotransferase), Alkaline phosphatase, GGT, (gamma-glutamyltranspeptidase) Bilirubin, Albumin and Prothrombin time. Further diseasecriteria correspond to hepatic encephalopathy, medication usage,ascites, graft failure, retransplantation, hospitalization,complications of immunosuppression, results of diagnostic tests, resultsof radiological testing, death and histological rejection on graftbiopsy. In addition, urine can be utilized for at the target tissue forprofiling in renal transplant, while biliary and intestinal secretionsand feces may be used favorably for hepatic or intestinal organallograft rejection. Diagnostic nucleotide sets can also be discoveredand developed for the diagnosis and monitoring of chronic renalallograft rejection.

In the case of renal allografts, gene expression markers may beidentified that are secreted proteins. These proteins may be detected inthe urine of allograft recipients using standard immunoassays. Proteinsare more likely to be present in the urine if they are of low molecularweight. Lower molecular weight proteins are more likely to pass throughthe glomerular membrane and into the urine.

In another example, diagnostic nucleotide sets are developed andvalidated for use in diagnosis and treatment of xenograft recipients.This can include the transplantation of any organ from a non-humananimal to a human or between non-human animals. Considerations fordiscovery and application of diagnostics and therapeutics and fordisease criterion are substantially similar to those for allografttransplantation between humans.

In another example, diagnostic nucleotide sets are developed andvalidated for use in diagnosis and treatment of artificial organrecipients. This includes, but is not limited to mechanical circulatorysupport, artificial hearts, left ventricular assist devices, renalreplacement therapies, organ prostheses and the like. Disease criteriaare thrombosis (blood clots), infection, death, hospitalization, andworsening measures of organ function (e.g., hemodynamics, creatinine,liver function testing, renal function testing, functional capacity).

In another example, diagnostic nucleotide sets are developed andvalidated for use in matching donor organs to appropriate recipients.Diagnostic gene set can be discovered that correlate with successfulmatching of donor organ to recipient. Disease criteria include graftfailure, acute and chronic rejection, death, hospitalization,immunosuppressive drug use, and complications of immunosuppression. Genesets may be assayed from the donor or recipient's peripheral blood,organ tissue or some other tissue.

In another example, diagnostic nucleotide sets are developed andvalidated for use in diagnosis and induction of patient immune tolerance(decrease rejection of an allograft by the host immune system). Diseasecriteria include rejection, assays of immune activation, need forimmunosuppression and all disease criteria noted above fortransplantation of each organ.

Viral Diseases

Diagnostic leukocyte nucleotide sets may be developed and validated foruse in diagnosing viral disease, as well as diagnosing and monitoringtransplant rejection. In another aspect, viral nucleotide sequences maybe added to a leukocyte nucleotide set for use in diagnosis of viraldiseases, as well as diagnosing and monitoring transplant rejection.Alternatively, viral nucleotide sets and leukocyte nucleotides sets maybe used sequentially.

Epstein-Barr Virus (EBV)

EBV causes a variety of diseases such as mononucleosis, B-cell lymphoma,and pharyngeal carcinoma. It infects mononuclear cells and circulatingatypical lymphocytes are a common manifestation of infection. Peripheralleukocyte gene expression is altered by infection. Transplant recipientsand patients who are immunosuppressed are at increased risk forEBV-associated lymphoma.

Diagnostic nucleotide sets may be developed and validated for use indiagnosis and monitoring of EBV, as well as diagnosing and monitoringtransplant rejection. In one aspect, the diagnostic nucleotide set is aleukocyte nucleotide set. Alternatively, EBV nucleotide sequences areadded to a leukocyte nucleotide set, for use in diagnosing EBV. Diseasecriteria correspond with diagnosis of EBV, and, in patients who areEBV-sero-positive, presence (or prospective occurrence) of EBV-relatedillnesses such as mononucleosis, and EBV-associated lymphoma. Diagnosticnucleotide sets are useful for diagnosis of EBV, and prediction ofoccurrence of EBV-related illnesses.

Cytomegalovirus (CMV)

Cytomegalovirus cause inflammation and disease in almost any tissue,particularly the colon, lung, bone marrow and retina, and is a veryimportant cause of disease in immunosuppressed patients, e.g.transplant, cancer, AIDS. Many patients are infected with or have beenexposed to CMV, but not all patients develop clinical disease from thevirus. Also, CMV negative recipients of allografts that come from CMVpositive donors are at high risk for CMV infection. As immunosuppressivedrugs are developed and used, it is increasingly important to identifypatients with current or impending clinical CMV disease, because thepotential benefit of immunosuppressive therapy must be balanced with theincreased rate of clinical CMV infection and disease that may resultfrom the use of immunosuppression therapy. CMV may also play a role inthe occurrence of atherosclerosis or restenosis after angioplasty. CMVexpression also correlates to transplant rejection, and is useful indiagnosing and monitoring transplant rejection.

Diagnostic nucleotide sets are developed for use in diagnosis andmonitoring of CMV infection or re-activation of CMV infection. In oneaspect, the diagnostic nucleotide set is a leukocyte nucleotide set. Inanother aspect, CMV nucleotide sequences are added to a leukocytenucleotide set, for use in diagnosing CMV. Disease criteria correspondto diagnosis of CMV (e.g., sero-positive state) and presence ofclinically active CMV. Disease criteria may also correspond toprospective data, e.g. the likelihood that CMV will become clinicallyactive or impending clinical CMV infection. Antiviral medications areavailable and diagnostic nucleotide sets can be used to select patientsfor early treatment, chronic suppression or prophylaxis of CMV activity.

Hepatitis B and C

These chronic viral infections affect about 1.25 and 2.7 millionpatients in the US, respectively. Many patients are infected, but sufferno clinical manifestations. Some patients with infection go on to sufferfrom chronic liver failure, cirrhosis and hepatic carcinoma.

Diagnostic nucleotide sets are developed for use in diagnosis andmonitoring of HBV or HCV infection. In one aspect, the diagnosticnucleotide set is a leukocyte nucleotide set. In another aspect, viralnucleotide sequences are added to a leukocyte nucleotide set, for use indiagnosing the virus and monitoring progression of liver disease.Disease criteria correspond to diagnosis of the virus (e.g.,sero-positive state or other disease symptoms). Alternatively, diseasecriteria correspond to liver damage, e.g., elevated alkalinephosphatase, ALT, AST or evidence of ongoing hepatic damage on liverbiopsy. Alternatively, disease criteria correspond to serum liver tests(AST, ALT, Alkaline Phosphatase, GGT, PT, bilirubin), liver biopsy,liver ultrasound, viral load by serum PCR, cirrhosis, hepatic cancer,need for hospitalization or listing for liver transplant. Diagnosticnucleotide sets are used to diagnose HBV and HCV, and to predictlikelihood of disease progression. Antiviral therapeutic usage, such asInterferon gamma and Ribavirin, can also be disease criteria.

HIV

HIV infects T cells and certainly causes alterations in leukocyteexpression. Diagnostic nucleotide sets are developed for diagnosis andmonitoring of HIV. In one aspect, the diagnostic nucleotide set is aleukocyte nucleotide set. In another aspect, viral nucleotide sequencesare added to a leukocyte nucleotide set, for use in diagnosing thevirus. Disease criteria correspond to diagnosis of the virus (e.g.,sero-positive state). In addition, disease criteria correspond to viralload, CD4 T cell counts, opportunistic infection, response toantiretroviral therapy, progression to AIDS, rate of progression and theoccurrence of other HIV related outcomes (e.g., malignancy, CNSdisturbance). Response to antiretrovirals may also be disease criteria.

Pharmacogenomics

Pharmacogenomics is the study of the individual propensity to respond toa particular drug therapy (combination of therapies). In this context,response can mean whether a particular drug will work on a particularpatient, e.g. some patients respond to one drug but not to another drug.Response can also refer to the likelihood of successful treatment or theassessment of progress in treatment. Titration of drug therapy to aparticular patient is also included in this description, e.g. differentpatients can respond to different doses of a given medication. Thisaspect may be important when drugs with side-effects or interactionswith other drug therapies are contemplated.

Diagnostic nucleotide sets are developed and validated for use inassessing whether a patient will respond to a particular therapy and/ormonitoring response of a patient to drug therapy (therapies). Diseasecriteria correspond to presence or absence of clinical symptoms orclinical endpoints, presence of side-effects or interaction with otherdrug(s). The diagnostic nucleotide set may further comprise nucleotidesequences that are targets of drug treatment or markers of activedisease.

Validation and Accuracy of Diagnostic Nucleotide Sets

Prior to widespread application of the diagnostic probe sets of theinvention the predictive value of the probe set is validated. When thediagnostic probe set is discovered by microarray based expressionanalysis, the differential expression of the member genes may bevalidated by a less variable and more quantitative and accuratetechnology such as real time PCR. In this type of experiment theamplification product is measured during the PCR reaction. This enablesthe researcher to observe the amplification before any reagent becomesrate limiting for amplification. In kinetic PCR the measurement is ofC_(T) (threshold cycle) or C_(P) (crossing point). This measurement(C_(T)=C_(P)) is the point at which an amplification curve crosses athreshold fluorescence value. The threshold is set to a point within thearea where all of the reactions were in their linear phase ofamplification. When measuring C_(T), a lower C_(T) value is indicativeof a higher amount of starting material since an earlier cycle numbermeans the threshold was crossed more quickly.

Several fluorescence methodologies are available to measureamplification product in real-time PCR. Taqman (Applied BioSystems,Foster City, Calif.) uses fluorescence resonance energy transfer (FRET)to inhibit signal from a probe until the probe is degraded by thesequence specific binding and Taq 3′ exonuclease activity. MolecularBeacons (Stratagene, La Jolla, Calif.) also use FRET technology, wherebythe fluorescence is measured when a hairpin structure is relaxed by thespecific probe binding to the amplified DNA. The third commonly usedchemistry is Sybr Green, a DNA-binding dye (Molecular Probes, Eugene,Oreg.). The more amplified product that is produced, the higher thesignal. The Sybr Green method is sensitive to non-specific amplificationproducts, increasing the importance of primer design and selection.Other detection chemistries can also been used, such as ethedium bromideor other DNA-binding dyes and many modifications of the fluorescentdye/quencher dye Taqman chemistry, for example scorpions.

Real-time PCR validation can be done as described in Example 12.

Typically, the oligonucleotide sequence of each probe is confirmed, e.g.by DNA sequencing using an oligonucleotide-specific primer. Partialsequence obtained is generally sufficient to confirm the identity of theoligonucleotide probe. Alternatively, a complementary polynucleotide isfluorescently labeled and hybridized to the array, or to a differentarray containing a resynthesized version of the oligo nucleotide probe,and detection of the correct probe is confirmed.

Typically, validation is performed by statistically evaluating theaccuracy of the correspondence between the molecular signature for adiagnostic probe set and a selected indicator. For example, theexpression differential for a nucleotide sequence between two subjectclasses can be expressed as a simple ratio of relative expression. Theexpression of the nucleotide sequence in subjects with selectedindicator can be compared to the expression of that nucleotide sequencein subjects without the indicator, as described in the followingequations.ΣE _(x) ai/N=E _(x) Athe average expression of nucleotide sequence x in the members of groupA;ΣE _(x) bi/M=E _(x) Bthe average expression of nucleotide sequence x in the members of groupB;E _(x) A/ExB=ΔE _(x) ABthe average differential expression of nucleotide sequence x betweengroups A and B:where Σ indicates a sum; Ex is the expression of nucleotide sequence xrelative to a standard; ai are the individual members of group A, groupA has N members; bi are the individual members of group B, group B has Mmembers.

The expression of at least two nucleotide sequences, e.g., nucleotidesequence X and nucleotide sequence Y are measured relative to a standardin at least one subject of group A (e.g., with a disease) and group B(e.g., without the disease). Ideally, for purposes of validation theindicator is independent from (i.e., not assigned based upon) theexpression pattern. Alternatively, a minimum threshold of geneexpression for nucleotide sequences X and Y, relative to the standard,are designated for assignment to group A. For nucleotide sequence x,this threshold is designated ΔEx, and for nucleotide sequence y, thethreshold is designated ΔEy.

The following formulas are used in the calculations below:Sensitivity=(true positives/true positives+false negatives)Specificity=(true negatives/true negatives+false positives)

If, for example, expression of nucleotide sequence x above a threshold:x>ΔEx, is observed for 80/100 subjects in group A and for 10/100subjects in group B, the sensitivity of nucleotide sequence x for theassignment to group A, at the given expression threshold ΔEx, is 80%,and the specificity is 90%.

If the expression of nucleotide sequence y is >ΔEy in 80/100 subjects ingroup A, and in 10/100 subjects in group B, then, similarly thesensitivity of nucleotide sequence y for the assignment to group A atthe given threshold ΔEy is 80% and the specificity is 90%. If inaddition, 60 of the 80 subjects in group A that meet the expressionthreshold for nucleotide sequence y also meet the expression thresholdΔEx and that 5 of the 10 subjects in group B that meet the expressionthreshold for nucleotide sequence y also meet the expression thresholdΔEx, the sensitivity of the test (x>ΔEx and y>ΔEy) for assignment ofsubjects to group A is 60% and the specificity is 95%.

Alternatively, if the criteria for assignment to group A are change to:Expression of x>ΔEx or expression of y>ΔEy, the sensitivity approaches100% and the specificity is 85%.

Clearly, the predictive accuracy of any diagnostic probe set isdependent on the minimum expression threshold selected. The expressionof nucleotide sequence X (relative to a standard) is measured insubjects of groups A (with disease) and B (without disease). The minimumthreshold of nucleotide sequence expression for x, required forassignment to group A is designated ΔEx 1.

If 90/100 patients in group A have expression of nucleotide sequencex>ΔEx 1 and 20/100 patients in group B have expression of nucleotidesequence x>ΔEx 1, then the sensitivity of the expression of nucleotidesequence x (using ΔEx 1 as a minimum expression threshold) forassignment of patients to group A will be 90% and the specificity willbe 80%.

Altering the minimum expression threshold results in an alteration inthe specificity and sensitivity of the nucleotide sequences in question.For example, if the minimum expression threshold of nucleotide sequencex for assignment of subjects to group A is lowered to ΔEx 2, such that100/100 subjects in group A and 40/100 subjects in group B meet thethreshold, then the sensitivity of the test for assignment of subjectsto group A will be 100% and the specificity will be 60%.

Thus, for 2 nucleotide sequences X and Y: the expression of nucleotidesequence x and nucleotide sequence y (relative to a standard) aremeasured in subjects belonging to groups A (with disease) and B (withoutdisease). Minimum thresholds of nucleotide sequence expression fornucleotide sequences X and Y (relative to common standards) aredesignated for assignment to group A. For nucleotide sequence x, thisthreshold is designated ΔEx1 and for nucleotide sequence y, thisthreshold is designated ΔEy1.

If in group A, 90/100 patients meet the minimum requirements ofexpression ΔEx1 and ΔEy1, and in group B, 10/100 subjects meet theminimum requirements of expression ΔEx1 and ΔEy1, then the sensitivityof the test for assignment of subjects to group A is 90% and thespecificity is 90%.

Increasing the minimum expression thresholds for X and Y to ΔEx2 andΔEy2, such that in group A, 70/100 subjects meet the minimumrequirements of expression ΔEx2 and ΔEy2, and in group B, 3/100 subjectsmeet the minimum requirements of expression ΔEx2 and ΔEy2. Now thesensitivity of the test for assignment of subjects to group A is 70% andthe specificity is 97%.

If the criteria for assignment to group A is that the subject inquestion meets either threshold, ΔEx2 or ΔEy2, and it is found that100/100 subjects in group A meet the criteria and 20/100 subjects ingroup B meet the criteria, then the sensitivity of the test forassignment to group A is 100% and the specificity is 80%.

Individual components of a diagnostic probe set each have a definedsensitivity and specificity for distinguishing between subject groups.Such individual nucleotide sequences can be employed in concert as adiagnostic probe set to increase the sensitivity and specificity of theevaluation. The database of molecular signatures is queried byalgorithms to identify the set of nucleotide sequences (i.e.,corresponding to members of the probe set) with the highest averagedifferential expression between subject groups. Typically, as the numberof nucleotide sequences in the diagnostic probe set increases, so doesthe predictive value, that is, the sensitivity and specificity of theprobe set. When the probe sets are defined they may be used fordiagnosis and patient monitoring as discussed below. The diagnosticsensitivity and specificity of the probe sets for the defined use can bedetermined for a given probe set with specified expression levels asdemonstrated above. By altering the expression threshold required forthe use of each nucleotide sequence as a diagnostic, the sensitivity andspecificity of the probe set can be altered by the practitioner. Forexample, by lowering the magnitude of the expression differentialthreshold for each nucleotide sequence in the set, the sensitivity ofthe test will increase, but the specificity will decrease. As isapparent from the foregoing discussion, sensitivity and specificity areinversely related and the predictive accuracy of the probe set iscontinuous and dependent on the expression threshold set for eachnucleotide sequence. Although sensitivity and specificity tend to havean inverse relationship when expression thresholds are altered, bothparameters can be increased as nucleotide sequences with predictivevalue are added to the diagnostic nucleotide set. In addition a singleor a few markers may not be reliable expression markers across apopulation of patients. This is because of the variability in expressionand measurement of expression that exists between measurements,individuals and individuals over time. Inclusion of a large number ofcandidate nucleotide sequences or large numbers of nucleotide sequencesin a diagnostic nucleotide set allows for this variability as not allnucleotide sequences need to meet a threshold for diagnosis. Generally,more markers are better than a single marker. If many markers are usedto make a diagnosis, the likelihood that all expression markers will notmeet some thresholds based upon random variability is low and thus thetest will give fewer false negatives.

It is appreciated that the desired diagnostic sensitivity andspecificity of the diagnostic nucleotide set may vary depending on theintended use of the set. For example, in certain uses, high specificityand high sensitivity are desired. For example, a diagnostic nucleotideset for predicting which patient population may experience side effectsmay require high sensitivity so as to avoid treating such patients. Inother settings, high sensitivity is desired, while reduced specificitymay be tolerated. For example, in the case of a beneficial treatmentwith few side effects, it may be important to identify as many patientsas possible (high sensitivity) who will respond to the drug, andtreatment of some patients who will not respond is tolerated. In othersettings, high specificity is desired and reduced sensitivity may betolerated. For example, when identifying patients for an early-phaseclinical trial, it is important to identify patients who may respond tothe particular treatment. Lower sensitivity is tolerated in this settingas it merely results in reduced patients who enroll in the study orrequires that more patients are screened for enrollment.

Methods of Using Diagnostic Nucleotide Sets.

The invention also provide methods of using the diagnostic nucleotidesets to: diagnose disease; assess severity of disease; predict futureoccurrence of disease; predict future complications of disease;determine disease prognosis; evaluate the patient's risk, or “stratify”a group of patients; assess response to current drug therapy; assessresponse to current non-pharmacological therapy; determine the mostappropriate medication or treatment for the patient; predict whether apatient is likely to respond to a particular drug; and determine mostappropriate additional diagnostic testing for the patient, among otherclinically and epidemiologically relevant applications.

The nucleotide sets of the invention can be utilized for a variety ofpurposes by physicians, healthcare workers, hospitals, laboratories,patients, companies and other institutions. As indicated previously,essentially any disease, condition, or status for which at least onenucleotide sequence is differentially expressed in leukocyte populations(or sub-populations) can be evaluated, e.g., diagnosed, monitored, etc.using the diagnostic nucleotide sets and methods of the invention. Inaddition to assessing health status at an individual level, thediagnostic nucleotide sets of the present invention are suitable forevaluating subjects at a “population level,” e.g., for epidemiologicalstudies, or for population screening for a condition or disease.

Collection and Preparation of Sample

RNA, protein and/or DNA is prepared using methods well-known in the art,as further described herein. It is appreciated that subject samplescollected for use in the methods of the invention are generallycollected in a clinical setting, where delays may be introduced beforeRNA samples are prepared from the subject samples of whole blood, e.g.the blood sample may not be promptly delivered to the clinical lab forfurther processing. Further delay may be introduced in the clinical labsetting where multiple samples are generally being processed at anygiven time. For this reason, methods which feature lengthy incubationsof intact leukocytes at room temperature are not preferred, because theexpression profile of the leukocytes may change during this extendedtime period. For example, RNA can be isolated from whole blood using aphenol/guanidine isothiocyanate reagent or another direct whole-bloodlysis method, as described in, e.g., U.S. Pat. Nos. 5,346,994 and4,843,155. This method may be less preferred under certain circumstancesbecause the large majority of the RNA recovered from whole blood RNAextraction comes from erythrocytes since these cells outnumberleukocytes 1000:1. Care must be taken to ensure that the presence oferythrocyte RNA and protein does not introduce bias in the RNAexpression profile data or lead to inadequate sensitivity or specificityof probes.

Alternatively, intact leukocytes may be collected from whole blood usinga lysis buffer that selectively lyses erythrocytes, but not leukocytes,as described, e.g., in (U.S. Pat. Nos. 5,973,137, and 6,020,186). Intactleukocytes are then collected by centrifugation, and leukocyte RNA isisolated using standard protocols, as described herein. However, thismethod does not allow isolation of sub-populations of leukocytes, e.g.mononuclear cells, which may be desired. In addition, the expressionprofile may change during the lengthy incubation in lysis buffer,especially in a busy clinical lab where large numbers of samples arebeing prepared at any given time.

Alternatively, specific leukocyte cell types can be separated usingdensity gradient reagents (Boyum, A, 1968.). For example, mononuclearcells may be separated from whole blood using density gradientcentrifugation, as described, e.g., in U.S. Pat. Nos. 4,190,535,4,350,593, 4,751,001, 4,818,418, and 5,053,134. Blood is drawn directlyinto a tube containing an anticoagulant and a density reagent (such asFicoll or Percoll). Centrifugation of this tube results in separation ofblood into an erythrocyte and granulocyte layer, a mononuclear cellsuspension, and a plasma layer. The mononuclear cell layer is easilyremoved and the cells can be collected by centrifugation, lysed, andfrozen. Frozen samples are stable until RNA can be isolated. Densitycentrifugation, however, must be conducted at room temperature, and ifprocessing is unduly lengthy, such as in a busy clinical lab, theexpression profile may change.

Alternatively, cells can be separated using fluorescence activated cellsorting (FACS) or some other technique, which divides cells into subsetsbased on gene or protein expression. This may be desirable to enrich thesample for cells of interest, but it may also introduce cellmanipulations and time delays, which result in alteration of geneexpression profiles (Cantor et al. 1975; Galbraith et al. 1999).

The quality and quantity of each clinical RNA sample is desirablychecked before amplification and labeling for array hybridization, usingmethods known in the art. For example, one microliter of each sample maybe analyzed on a Bioanalyzer (Agilent 2100 Palo Alto, Calif. USA) usingan RNA 6000 nano LabChip (Caliper, Mountain View, Calif. USA). DegradedRNA is identified by the reduction of the 28S to 18S ribosomal RNA ratioand/or the presence of large quantities of RNA in the 25-100 nucleotiderange.

It is appreciated that the RNA sample for use with a diagnosticnucleotide set may be produced from the same or a different cellpopulation, sub-population and/or cell type as used to identify thediagnostic nucleotide set. For example, a diagnostic nucleotide setidentified using RNA extracted from mononuclear cells may be suitablefor analysis of RNA extracted from whole blood or mononuclear cells,depending on the particular characteristics of the members of thediagnostic nucleotide set. Generally, diagnostic nucleotide sets must betested and validated when used with RNA derived from a different cellpopulation, sub-population or cell type than that used when obtainingthe diagnostic gene set. Factors such as the cell-specific geneexpression of diagnostic nucleotide set members, redundancy of theinformation provided by members of the diagnostic nucleotide set,expression level of the member of the diagnostic nucleotide set, andcell-specific alteration of expression of a member of the diagnosticnucleotide set will contribute to the usefulness of using a differentRNA source than that used when identifying the members of the diagnosticnucleotide set. It is appreciated that it may be desirable to assay RNAderived from whole blood, obviating the need to isolate particular celltypes from the blood.

Rapid Method of RNA Extraction Suitable for Production in a ClinicalSetting of High Quality RNA for Expression Profiling

In a clinical setting, obtaining high quality RNA preparations suitablefor expression profiling, from a desired population of leukocytes posescertain technical challenges, including: the lack of capacity for rapid,high-throughput sample processing in the clinical setting, and thepossibility that delay in processing (in a busy lab or in the clinicalsetting) may adversely affect RNA quality, e.g. by a permitting theexpression profile of certain nucleotide sequences to shift. Also, useof toxic and expensive reagents, such as phenol, may be disfavored inthe clinical setting due to the added expense associated with shippingand handling such reagents.

A useful method for RNA isolation for leukocyte expression profilingwould allow the isolation of monocyte and lymphocyte RNA in a timelymanner, while preserving the expression profiles of the cells, andallowing inexpensive production of reproducible high-quality RNAsamples. Accordingly, the invention provides a method of addinginhibitor(s) of RNA transcription and/or inhibitor(s) of proteinsynthesis, such that the expression profile is “frozen” and RNAdegradation is reduced. A desired leukocyte population or sub-populationis then isolated, and the sample may be frozen or lysed before furtherprocessing to extract the RNA. Blood is drawn from subject populationand exposed to ActinomycinD (to a final concentration of 10 ug/ml) toinhibit transcription, and cycloheximide (to a final concentration of 10ug/ml) to inhibit protein synthesis. The inhibitor(s) can be injectedinto the blood collection tube in liquid form as soon as the blood isdrawn, or the tube can be manufactured to contain either lyophilizedinhibitors or inhibitors that are in solution with the anticoagulant. Atthis point, the blood sample can be stored at room temperature until thedesired leukocyte population or sub-population is isolated, as describedelsewhere. RNA is isolated using standard methods, e.g., as describedabove, or a cell pellet or extract can be frozen until furtherprocessing of RNA is convenient.

The invention also provides a method of using a low-temperature densitygradient for separation of a desired leukocyte sample. In anotherembodiment, the invention provides the combination of use of alow-temperature density gradient and the use of transcriptional and/orprotein synthesis inhibitor(s). A desired leukocyte population isseparated using a density gradient solution for cell separation thatmaintains the required density and viscosity for cell separation at 0-4°C. Blood is drawn into a tube containing this solution and may berefrigerated before and during processing as the low temperatures slowcellular processes and minimize expression profile changes. Leukocytesare separated, and RNA is isolated using standard methods. Alternately,a cell pellet or extract is frozen until further processing of RNA isconvenient. Care must be taken to avoid rewarming the sample duringfurther processing steps.

Alternatively, the invention provides a method of using low-temperaturedensity gradient separation, combined with the use of actinomycin A andcyclohexamide, as described above.

Assessing Expression for Diagnostics

Expression profiles for the set of diagnostic nucleotide sequences in asubject sample can be evaluated by any technique that determines theexpression of each component nucleotide sequence. Methods suitable forexpression analysis are known in the art, and numerous examples arediscussed in the Sections titled “Methods of obtaining expression data”and “high throughput expression Assays”, above.

In many cases, evaluation of expression profiles is most efficiently,and cost effectively, performed by analyzing RNA expression.Alternatively, the proteins encoded by each component of the diagnosticnucleotide set are detected for diagnostic purposes by any techniquecapable of determining protein expression, e.g., as described above.Expression profiles can be assessed in subject leukocyte sample usingthe same or different techniques as those used to identify and validatethe diagnostic nucleotide set. For example, a diagnostic nucleotide setidentified as a subset of sequences on a cDNA microarray can be utilizedfor diagnostic (or prognostic, or monitoring, etc.) purposes on the samearray from which they were identified. Alternatively, the diagnosticnucleotide sets for a given disease or condition can be organized onto adedicated sub-array for the indicated purpose. It is important to notethat if diagnostic nucleotide sets are discovered using one technology,e.g. RNA expression profiling, but applied as a diagnostic using anothertechnology, e.g. protein expression profiling, the nucleotide sets mustgenerally be validated for diagnostic purposes with the new technology.In addition, it is appreciated that diagnostic nucleotide sets that aredeveloped for one use, e.g. to diagnose a particular disease, may laterbe found to be useful for a different application, e.g. to predict thelikelihood that the particular disease will occur. Generally, thediagnostic nucleotide set will need to be validated for use in thesecond circumstance. As discussed herein, the sequence of diagnosticnucleotide set members may be amplified from RNA or cDNA using methodsknown in the art providing specific amplification of the nucleotidesequences.

General Protein Methods

Protein products of the nucleotide sequences of the invention mayinclude proteins that represent functionally equivalent gene products.Such an equivalent gene product may contain deletions, additions orsubstitutions of amino acid residues within the amino acid sequenceencoded by the nucleotide sequences described, above, but which resultin a silent change, thus producing a functionally equivalent nucleotidesequence product. Amino acid substitutions may be made on the basis ofsimilarity in polarity, charge, solubility, hydrophobicity,hydrophilicity, and/or the amphipathic nature of the residues involved.

For example, nonpolar (hydrophobic) amino acids include alanine,leucine, isoleucine, valine, proline, phenylalanine, tryptophan, andmethionine; polar neutral amino acids include glycine, serine,threonine, cysteine, tyrosine, asparagine, and glutamine; positivelycharged (basic) amino acids include arginine, lysine, and histidine; andnegatively charged (acidic) amino acids include aspartic acid andglutamic acid. “Functionally equivalent”, as utilized herein, refers toa protein capable of exhibiting a substantially similar in vivo activityas the endogenous gene products encoded by the nucleotide described,above.

The gene products (protein products of the nucleotide sequences) may beproduced by recombinant DNA technology using techniques well known inthe art. Thus, methods for preparing the gene polypeptides and peptidesof the invention by expressing nucleic acid encoding nucleotidesequences are described herein. Methods which are well known to thoseskilled in the art can be used to construct expression vectorscontaining nucleotide sequence protein coding sequences and appropriatetranscriptional/translational control signals. These methods include,for example, in vitro recombinant DNA techniques, synthetic techniquesand in vivo recombination/genetic recombination. See, for example, thetechniques described in Sambrook et al., 1989, supra, and Ausubel etal., 1989, supra. Alternatively, RNA capable of encoding nucleotidesequence protein sequences may be chemically synthesized using, forexample, synthesizers. See, for example, the techniques described in“Oligonucleotide Synthesis”, 1984, Gait, M. J. ed., IRL Press, Oxford,which is incorporated by reference herein in its entirety

A variety of host-expression vector systems may be utilized to expressthe nucleotide sequence coding sequences of the invention. Suchhost-expression systems represent vehicles by which the coding sequencesof interest may be produced and subsequently purified, but alsorepresent cells which may, when transformed or transfected with theappropriate nucleotide coding sequences, exhibit the protein encoded bythe nucleotide sequence of the invention in situ. These include but arenot limited to microorganisms such as bacteria (e.g., E. coli, B.subtilis) transformed with recombinant bacteriophage DNA, plasmid DNA orcosmid DNA expression vectors containing nucleotide sequence proteincoding sequences; yeast (e.g. Saccharomyces, Pichia) transformed withrecombinant yeast expression vectors containing the nucleotide sequenceprotein coding sequences; insect cell systems infected with recombinantvirus expression vectors (e.g., baculovirus) containing the nucleotidesequence protein coding sequences; plant cell systems infected withrecombinant virus expression vectors (e.g., cauliflower mosaic virus,CaMV; tobacco mosaic virus, TMV) or transformed with recombinant plasmidexpression vectors (e.g., Ti plasmid) containing nucleotide sequenceprotein coding sequences; or mammalian cell systems (e.g. COS, CHO, BHK,293, 3T3) harboring recombinant expression constructs containingpromoters derived from the genome of mammalian cells (e.g.,metallothionein promoter) or from mammalian viruses (e.g., theadenovirus late promoter; the vaccinia virus 7.5 K promoter).

In bacterial systems, a number of expression vectors may beadvantageously selected depending upon the use intended for thenucleotide sequence protein being expressed. For example, when a largequantity of such a protein is to be produced, for the generation ofantibodies or to screen peptide libraries, for example, vectors whichdirect the expression of high levels of fusion protein products that arereadily purified may be desirable. Such vectors include, but are notlimited, to the E. coli expression vector pUR278 (Ruther et al., 1983,EMBO J. 2:1791), in which the nucleotide sequence protein codingsequence may be ligated individually into the vector in frame with thelac Z coding region so that a fusion protein is produced; pIN vectors(Inouye & Inouye, 1985, Nucleic Acids Res. 13:3101-3109; Van Heeke &Schuster, 1989, J. Biol. Chem. 264:5503-5509); and the likes of pGEXvectors may also be used to express foreign polypeptides as fusionproteins with glutathione S-transferase (GST). In general, such fusionproteins are soluble and can easily be purified from lysed cells byadsorption to glutathione-agarose beads followed by elution in thepresence of free glutathione. The pGEX vectors are designed to includethrombin or factor Xa protease cleavage sites so that the cloned targetnucleotide sequence protein can be released from the GST moiety. Othersystems useful in the invention include use of the FLAG epitope or the6-HIS systems.

In an insect system, Autographa californica nuclear polyhedrosis virus(AcNPV) is used as a vector to express foreign nucleotide sequences. Thevirus grows in Spodoptera frugiperda cells. The nucleotide sequencecoding sequence may be cloned individually into non-essential regions(for example the polyhedrin gene) of the virus and placed under controlof an AcNPV promoter (for example the polyhedrin promoter). Successfulinsertion of nucleotide sequence coding sequence will result ininactivation of the polyhedrin gene and production of non-occludedrecombinant virus (i.e., virus lacking the proteinaceous coat coded forby the polyhedrin gene). These recombinant viruses are then used toinfect Spodoptera frugiperda cells in which the inserted nucleotidesequence is expressed. (E.g., see Smith et al., 1983, J. Virol. 46: 584;Smith, U.S. Pat. No. 4,215,051).

In mammalian host cells, a number of viral-based expression systems maybe utilized. In cases where an adenovirus is used as an expressionvector, the nucleotide sequence coding sequence of interest may beligated to an adenovirus transcription/translation control complex,e.g., the late promoter and tripartite leader sequence. This chimericnucleotide sequence may then be inserted in the adenovirus genome by invitro or in vivo recombination. Insertion in a non-essential region ofthe viral genome (e.g., region E1 or E3) will result in a recombinantvirus that is viable and capable of expressing nucleotide sequenceencoded protein in infected hosts. (E.g., See Logan & Shenk, 1984, Proc.Natl. Acad. Sci. USA 81:3655-3659). Specific initiation signals may alsobe required for efficient translation of inserted nucleotide sequencecoding sequences. These signals include the ATG initiation codon andadjacent sequences. In cases where an entire nucleotide sequence,including its own initiation codon and adjacent sequences, is insertedinto the appropriate expression vector, no additional translationalcontrol signals may be needed. However, in cases where only a portion ofthe nucleotide sequence coding sequence is inserted, exogenoustranslational control signals, including, perhaps, the ATG initiationcodon, must be provided. Furthermore, the initiation codon must be inphase with the reading frame of the desired coding sequence to ensuretranslation of the entire insert. These exogenous translational controlsignals and initiation codons can be of a variety of origins, bothnatural and synthetic. The efficiency of expression may be enhanced bythe inclusion of appropriate transcription enhancer elements,transcription terminators, etc. (see Bittner et al., 1987, Methods inEnzymol. 153:516-544).

In addition, a host cell strain may be chosen which modulates theexpression of the inserted sequences, or modifies and processes theproduct of the nucleotide sequence in the specific fashion desired. Suchmodifications (e.g., glycosylation) and processing (e.g., cleavage) ofprotein products may be important for the function of the protein.Different host cells have characteristic and specific mechanisms for thepost-translational processing and modification of proteins. Appropriatecell lines or host systems can be chosen to ensure the correctmodification and processing of the foreign protein expressed. To thisend, eukaryotic host cells which possess the cellular machinery forproper processing of the primary transcript, glycosylation, andphosphorylation of the gene product may be used. Such mammalian hostcells include but are not limited to CHO, VERO, BHK, HeLa, COS, MDCK,293, 3T3, WI38, etc.

For long-term, high-yield production of recombinant proteins, stableexpression is preferred. For example, cell lines which stably expressthe nucleotide sequence encoded protein may be engineered. Rather thanusing expression vectors which contain viral origins of replication,host cells can be transformed with DNA controlled by appropriateexpression control elements (e.g., promoter, enhancer, sequences,transcription terminators, polyadenylation sites, etc.), and aselectable marker. Following the introduction of the foreign DNA,engineered cells may be allowed to grow for 1-2 days in an enrichedmedia, and then are switched to a selective media. The selectable markerin the recombinant plasmid confers resistance to the selection andallows cells to stably integrate the plasmid into their chromosomes andgrow to form foci which in turn can be cloned and expanded into celllines. This method may advantageously be used to engineer cell lineswhich express nucleotide sequence encoded protein. Such engineered celllines may be particularly useful in screening and evaluation ofcompounds that affect the endogenous activity of the nucleotide sequenceencoded protein.

A number of selection systems may be used, including but not limited tothe herpes simplex virus thymidine kinase (Wigler, et al., 1977, Cell11:223), hypoxanthine-guanine phosphoribosyltransferase (Szybalska &Szybalski, 1962, Proc. Natl. Acad. Sci. USA 48:2026), and adeninephosphoribosyltransferase (Lowy, et al., 1980, Cell 22:817) genes can beemployed in tk-, hgprt- or aprt-cells, respectively. Also,antimetabolite resistance can be used as the basis of selection fordhfr, which confers resistance to methotrexate (Wigler, et al., 1980,Natl. Acad. Sci. USA 77:3567; O'Hare, et al., 1981, Proc. Natl. Acad.Sci. USA 78:1527); gpt, which confers resistance to mycophenolic acid(Mulligan & Berg, 1981, Proc. Natl. Acad. Sci. USA 78:2072); neo, whichconfers resistance to the aminoglycoside G-418 (Colberre-Garapin, etal., 1981, J. Mol. Biol. 150:1); and hygro, which confers resistance tohygromycin (Santerre, et al., 1984, Gene 30:147) genes.

An alternative fusion protein system allows for the ready purificationof non-denatured fusion proteins expressed in human cell lines(Janknecht, et al., 1991, Proc. Natl. Acad. Sci. USA 88: 8972-8976). Inthis system, the nucleotide sequence of interest is subcloned into avaccinia recombination plasmid such that the nucleotide sequence's openreading frame is translationally fused to an amino-terminal tagconsisting of six histidine residues. Extracts from cells infected withrecombinant vaccinia virus are loaded onto Ni.sup.2+−nitriloaceticacid-agarose columns and histidine-tagged proteins are selectivelyeluted with imidazole-containing buffers.

Where recombinant DNA technology is used to produce the protein encodedby the nucleotide sequence for such assay systems, it may beadvantageous to engineer fusion proteins that can facilitate labeling,immobilization and/or detection.

Antibodies

Indirect labeling involves the use of a protein, such as a labeledantibody, which specifically binds to the protein encoded by thenucleotide sequence. Such antibodies include but are not limited topolyclonal, monoclonal, chimeric, single chain, Fab fragments andfragments produced by an Fab expression library.

The invention also provides for antibodies to the protein encoded by thenucleotide sequences. Described herein are methods for the production ofantibodies capable of specifically recognizing one or more nucleotidesequence epitopes. Such antibodies may include, but are not limited topolyclonal antibodies, monoclonal antibodies (mAbs), humanized orchimeric antibodies, single chain antibodies, Fab fragments, F(ab′)2fragments, fragments produced by a Fab expression library,anti-idiotypic (anti-Id) antibodies, and epitope-binding fragments ofany of the above. Such antibodies may be used, for example, in thedetection of a nucleotide sequence in a biological sample, or,alternatively, as a method for the inhibition of abnormal gene activity,for example, the inhibition of a disease target nucleotide sequence, asfurther described below. Thus, such antibodies may be utilized as partof cardiovascular or other disease treatment method, and/or may be usedas part of diagnostic techniques whereby patients may be tested forabnormal levels of nucleotide sequence encoded proteins, or for thepresence of abnormal forms of the such proteins.

For the production of antibodies to a nucleotide sequence, various hostanimals may be immunized by injection with a protein encoded by thenucleotide sequence, or a portion thereof. Such host animals may includebut are not limited to rabbits, mice, and rats, to name but a few.Various adjuvants may be used to increase the immunological response,depending on the host species, including but not limited to Freund's(complete and incomplete), mineral gels such as aluminum hydroxide,surface active substances such as lysolecithin, pluronic polyols,polyanions, peptides, oil emulsions, keyhole limpet hemocyanin,dinitrophenol, and potentially useful human adjuvants such as BCG(bacille Calmette-Guerin) and Corynebacterium parvum.

Polyclonal antibodies are heterogeneous populations of antibodymolecules derived from the sera of animals immunized with an antigen,such as gene product, or an antigenic functional derivative thereof. Forthe production of polyclonal antibodies, host animals such as thosedescribed above, may be immunized by injection with gene productsupplemented with adjuvants as also described above.

Monoclonal antibodies, which are homogeneous populations of antibodiesto a particular antigen, may be obtained by any technique which providesfor the production of antibody molecules by continuous cell lines inculture. These include, but are not limited to the hybridoma techniqueof Kohler and Milstein, (1975, Nature 256:495-497; and U.S. Pat. No.4,376,110), the human B-cell hybridoma technique (Kosbor et al., 1983,Immunology Today 4:72; Cole et al., 1983, Proc. Natl. Acad. Sci. USA80:2026-2030), and the EBV-hybridoma technique (Cole et al., 1985,Monoclonal Antibodies And Cancer Therapy, Alan R. Liss, Inc., pp.77-96). Such antibodies may be of any immunoglobulin class includingIgG, IgM, IgE, IgA, IgD and any subclass thereof. The hybridomaproducing the mAb of this invention may be cultivated in vitro or invivo.

In addition, techniques developed for the production of “chimericantibodies” (Morrison et al., 1984, Proc. Natl. Acad. Sci.,81:6851-6855; Neuberger et al., 1984, Nature, 312:604-608; Takeda etal., 1985, Nature, 314:452-454) by splicing the genes from a mouseantibody molecule of appropriate antigen specificity together with genesfrom a human antibody molecule of appropriate biological activity can beused. A chimeric antibody is a molecule in which different portions arederived from different animal species, such as those having a variableregion derived from a murine mAb and a human immunoglobulin constantregion.

Alternatively, techniques described for the production of single chainantibodies (U.S. Pat. No. 4,946,778; Bird, 1988, Science 242:423-426;Huston et al., 1988, Proc. Natl. Acad. Sci. USA 85:5879-5883; and Wardet al., 1989, Nature 334:544-546) can be adapted to produce nucleotidesequence-single chain antibodies. Single chain antibodies are formed bylinking the heavy and light chain fragments of the Fv region via anamino acid bridge, resulting in a single chain polypeptide.

Antibody fragments which recognize specific epitopes may be generated byknown techniques For example, such fragments include but are not limitedto: the F(ab′)2 fragments which can be produced by pepsin digestion ofthe antibody molecule and the Fab fragments which can be generated byreducing the disulfide bridges of the F(ab′)2 fragments. Alternatively,Fab expression libraries may be constructed (Huse et al., 1989, Science,246:1275-1281) to allow rapid and easy identification of monoclonal Fabfragments with the desired specificity.

Disease Specific Target Nucleotide Sequences

The invention also provides disease specific target nucleotidesequences, and sets of disease specific target nucleotide sequences. Thediagnostic nucleotide sets, subsets thereof, novel nucleotide sequences,and individual members of the diagnostic nucleotide sets identified asdescribed above are also disease specific target nucleotide sequences.In particular, individual nucleotide sequences that are differentiallyregulated or have predictive value that is strongly correlated with adisease or disease criterion are especially favorable as diseasespecific target nucleotide sequences. Sets of genes that areco-regulated may also be identified as disease specific targetnucleotide sets. Such nucleotide sequences and/or nucleotide sequenceproducts are targets for modulation by a variety of agents andtechniques. For example, disease specific target nucleotide sequences(or the products of such nucleotide sequences, or sets of diseasespecific target nucleotide sequences) can be inhibited or activated by,e.g., target specific monoclonal antibodies or small moleculeinhibitors, or delivery of the nucleotide sequence or gene product ofthe nucleotide sequence to patients. Also, sets of genes can beinhibited or activated by a variety of agents and techniques. Thespecific usefulness of the target nucleotide sequence(s) depends on thesubject groups from which they were discovered, and the disease ordisease criterion with which they correlate.

Imaging

The invention also provides for imaging reagents. The differentiallyexpressed leukocyte nucleotide sequences, diagnostic nucleotide sets, orportions thereof, and novel nucleotide sequences of the invention arenucleotide sequences expressed in cells with or without disease.Leukocytes expressing a nucleotide sequence(s) that is differentiallyexpressed in a disease condition may localize within the body to sitesthat are of interest for imaging purposes. For example, a leukocyteexpressing a nucleotide sequence(s) that are differentially expressed inan individual having atherosclerosis may localize or accumulate at thesite of an atherosclerotic plaque. Such leukocytes, when labeled, mayprovide a detection reagent for use in imaging regions of the body wherelabeled leukocyte accumulate or localize, for example, at theatherosclerotic plaque in the case of atherosclerosis. For example,leukocytes are collected from a subject, labeled in vitro, andreintroduced into a subject. Alternatively, the labeled reagent isintroduced into the subject individual, and leukocyte labeling occurswithin the patient.

Imaging agents that detect the imaging targets of the invention areproduced by well-known molecular and immunological methods (forexemplary protocols, see, e.g., Ausubel, Berger, and Sambrook, as wellas Harlow and Lane, supra).

For example, a full-length nucleic acid sequence, or alternatively, agene fragment encoding an immunogenic peptide or polypeptide fragments,is cloned into a convenient expression vector, for example, a vectorincluding an in-frame epitope or substrate binding tag to facilitatesubsequent purification. Protein is then expressed from the cloned cDNAsequence and used to generate antibodies, or other specific bindingmolecules, to one or more antigens of the imaging target protein.Alternatively, a natural or synthetic polypeptide (or peptide) or smallmolecule that specifically binds (or is specifically bound to) theexpressed imaging target can be identified through well establishedtechniques (see, e.g., Mendel et al. (2006) Anticancer Drug Des15:29-41; Wilson (2000) Curr Med Chem 7:73-98; Hamby and Showwalter(1999) Pharmacol Ther 82:169-93; and Shimazawa et al. (1998) Curr OpinStruct Biol 8:451-8). The binding molecule, e.g., antibody, smallmolecule ligand, etc., is labeled with a contrast agent or otherdetectable label, e.g., gadolinium, iodine, or a gamma-emitting source.For in-vivo imaging of a disease process that involved leukocytes, thelabeled antibody is infused into a subject, e.g., a human patient oranimal subject, and a sufficient period of time is passed to permitbinding of the antibody to target cells. The subject is then imaged withappropriate technology such as MRI (when the label is gadolinium) orwith a gamma counter (when the label is a gamma emitter).

Identification of Nucleotide Sequence Involved in Leukocyte Adhesion

The invention also encompasses a method of identifying nucleotidesequences involved in leukocyte adhesion. The interaction between theendothelial cell and leukocyte is a fundamental mechanism of allinflammatory disorders, including the diagnosis and prognosis ofallograft rejection the diseases listed in Table 1. For example, thefirst visible abnormality in atherosclerosis is the adhesion to theendothelium and diapedesis of mononuclear cells (e.g., T-cell andmonocyte). Insults to the endothelium (for example, cytokines, tobacco,diabetes, hypertension and many more) lead to endothelial cellactivation. The endothelium then expresses adhesion molecules, whichhave counter receptors on mononuclear cells. Once the leukocytereceptors have bound the endothelial adhesion molecules, they stick tothe endothelium, roll a short distance, stop and transmigrate across theendothelium. A similar set of events occurs in both acute and chronicinflammation. When the leukocyte binds the endothelial adhesionmolecule, or to soluble cytokines secreted by endothelial or othercells, a program of gene expression is activated in the leukocyte. Thisprogram of expression leads to leukocyte rolling, firm adhesion andtransmigration into the vessel wall or tissue parenchyma. Inhibition ofthis process is highly desirable goal in anti-inflammatory drugdevelopment. In addition, leukocyte nucleotide sequences and epithelialcell nucleotide sequences, that are differentially expressed during thisprocess may be disease-specific target nucleotide sequences.

Human endothelial cells, e.g. derived from human coronary arteries,human aorta, human pulmonary artery, human umbilical vein ormicrovascular endothelial cells, are cultured as a confluent monolayer,using standard methods. Some of the endothelial cells are then exposedto cytokines or another activating stimuli such as oxidized LDL,hyperglycemia, shear stress, or hypoxia (Moser et al. 1992). Someendothelial cells are not exposed to such stimuli and serve as controls.For example, the endothelial cell monolayer is incubated with culturemedium containing 5 U/ml of human recombinant IL-1alpha or 10 ng/ml TNF(tumor necrosis factor), for a period of minutes to overnight. Theculture medium composition is changed or the flask is sealed to inducehypoxia. In addition, tissue culture plate is rotated to induce sheerstress.

Human T-cells and/or monocytes are cultured in tissue culture flasks orplates, with LGM-3 media from Clonetics. Cells are incubated at 37degree C., 5% CO2 and 95% humidity. These leukocytes are exposed to theactivated or control endothelial layer by adding a suspension ofleukocytes on to the endothelial cell monolayer. The endothelial cellmonolayer is cultured on a tissue culture treated plate/flask or on amicroporous membrane. After a variable duration of exposures, theendothelial cells and leukocytes are harvested separately by treatingall cells with trypsin and then sorting the endothelial cells from theleukocytes by magnetic affinity reagents to an endothelial cell specificmarker such as PECAM-1 (Stem Cell Technologies). RNA is extracted fromthe isolated cells by standard techniques. Leukocyte RNA is labeled asdescribed above, and hybridized to leukocyte candidate nucleotidelibrary. Epithelial cell RNA is also labeled and hybridized to theleukocyte candidate nucleotide library. Alternatively, the epithelialcell RNA is hybridized to a epithelial cell candidate nucleotidelibrary, prepared according to the methods described for leukocytecandidate libraries, above.

Hybridization to candidate nucleotide libraries will reveal nucleotidesequences that are up-regulated or down-regulated in leukocyte and/orepithelial cells undergoing adhesion. The differentially regulatednucleotide sequences are further characterized, e.g. by isolating andsequencing the full-length sequence, analysis of the DNA and predictedprotein sequence, and functional characterization of the protein productof the nucleotide sequence, as described above. Further characterizationmay result in the identification of leukocyte adhesion specific targetnucleotide sequences, which may be candidate targets for regulation ofthe inflammatory process. Small molecule or antibody inhibitors can bedeveloped to inhibit the target nucleotide sequence function. Suchinhibitors are tested for their ability to inhibit leukocyte adhesion inthe in vitro test described above.

Integrated Systems

Integrated systems for the collection and analysis of expressionprofiles, and molecular signatures, as well as for the compilation,storage and access of the databases of the invention, typically includea digital computer with software including an instruction set forsequence searching and analysis, and, optionally, high-throughput liquidcontrol software, image analysis software, data interpretation software,a robotic control armature for transferring solutions from a source to adestination (such as a detection device) operably linked to the digitalcomputer, an input device (e.g., a computer keyboard) for enteringsubject data to the digital computer, or to control analysis operationsor high throughput sample transfer by the robotic control armature.Optionally, the integrated system further comprises an image scanner fordigitizing label signals from labeled assay components, e.g., labelednucleic acid hybridized to a candidate library microarray. The imagescanner can interface with image analysis software to provide ameasurement of the presence or intensity of the hybridized label, i.e.,indicative of an on/off expression pattern or an increase or decrease inexpression.

Readily available computational hardware resources using standardoperating systems are fully adequate, e.g., a PC (Intel x86 or Pentiumchip-compatible DOS,™ OS2,™ WINDOWS,™ WINDOWS NT,™ WINDOWS95,™WINDOWS98,™ LINUX, or even Macintosh, Sun or PCs will suffice) for usein the integrated systems of the invention. Current art in softwaretechnology is similarly adequate (i.e., there are a multitude of matureprogramming languages and source code suppliers) for design, e.g., of anupgradeable open-architecture object-oriented heuristic algorithm, orinstruction set for expression analysis, as described herein. Forexample, software for aligning or otherwise manipulating molecularsignatures can be constructed by one of skill using a standardprogramming language such as Visual basic, Fortran, Basic, Java, or thelike, according to the methods herein.

Various methods and algorithms, including genetic algorithms and neuralnetworks, can be used to perform the data collection, correlation, andstorage functions, as well as other desirable functions, as describedherein. In addition, digital or analog systems such as digital or analogcomputer systems can control a variety of other functions such as thedisplay and/or control of input and output files.

For example, standard desktop applications such as word processingsoftware (e.g., Corel WordPerfect™ or Microsoft Word™) and databasesoftware (e.g., spreadsheet software such as Corel Quattro Pro™,Microsoft Excel™, or database programs such as Microsoft Access™ orParadox™) can be adapted to the present invention by inputting one ormore character string corresponding, e.g., to an expression pattern orprofile, subject medical or historical data, molecular signature, or thelike, into the software which is loaded into the memory of a digitalsystem, and carrying out the operations indicated in an instruction set,e.g., as exemplified in FIG. 2. For example, systems can include theforegoing software having the appropriate character string information,e.g., used in conjunction with a user interface in conjunction with astandard operating system such as a Windows, Macintosh or LINUX system.For example, an instruction set for manipulating strings of characters,either by programming the required operations into the applications orwith the required operations performed manually by a user (or both). Forexample, specialized sequence alignment programs such as PILEUP or BLASTcan also be incorporated into the systems of the invention, e.g., foralignment of nucleic acids or proteins (or corresponding characterstrings).

Software for performing the statistical methods required for theinvention, e.g., to determine correlations between expression profilesand subsets of members of the diagnostic nucleotide libraries, such asprogrammed embodiments of the statistical methods described above, arealso included in the computer systems of the invention. Alternatively,programming elements for performing such methods as principle componentanalysis (PCA) or least squares analysis can also be included in thedigital system to identify relationships between data. Exemplarysoftware for such methods is provided by Partek, Inc., St. Peter, Mo.;at the web site partek.com.

Any controller or computer optionally includes a monitor which caninclude, e.g., a flat panel display (e.g., active matrix liquid crystaldisplay, liquid crystal display), a cathode ray tube (“CRT”) display, oranother display system which serves as a user interface, e.g., to outputpredictive data. Computer circuitry, including numerous integratedcircuit chips, such as a microprocessor, memory, interface circuits, andthe like, is often placed in a casing or box which optionally alsoincludes a hard disk drive, a floppy disk drive, a high capacityremovable drive such as a writeable CD-ROM, and other common peripheralelements.

Inputting devices such as a keyboard, mouse, or touch sensitive screen,optionally provide for input from a user and for user selection, e.g.,of sequences or data sets to be compared or otherwise manipulated in therelevant computer system. The computer typically includes appropriatesoftware for receiving user instructions, either in the form of userinput into a set parameter or data fields (e.g., to input relevantsubject data), or in the form of preprogrammed instructions, e.g.,preprogrammed for a variety of different specific operations. Thesoftware then converts these instructions to appropriate language forinstructing the system to carry out any desired operation.

The integrated system may also be embodied within the circuitry of anapplication specific integrated circuit (ASIC) or programmable logicdevice (PLD). In such a case, the invention is embodied in a computerreadable descriptor language that can be used to create an ASIC or PLD.The integrated system can also be embodied within the circuitry or logicprocessors of a variety of other digital apparatus, such as PDAs, laptopcomputer systems, displays, image editing equipment, etc.

The digital system can comprise a learning component where expressionprofiles, and relevant subject data are compiled and monitored inconjunction with physical assays, and where correlations, e.g.,molecular signatures with predictive value for a disease, areestablished or refined. Successful and unsuccessful combinations areoptionally documented in a database to provide justification/preferencesfor user-base or digital system based selection of diagnostic nucleotidesets with high predictive accuracy for a specified disease or condition.

The integrated systems can also include an automated workstation. Forexample, such a workstation can prepare and analyze leukocyte RNAsamples by performing a sequence of events including: preparing RNA froma human blood sample; labeling the RNA with an isotopic or non-isotopiclabel; hybridizing the labeled RNA to at least one array comprising allor part of the candidate library; and detecting the hybridizationpattern. The hybridization pattern is digitized and recorded in theappropriate database.

Automated RNA Preparation Tool

The invention also includes an automated RNA preparation tool for thepreparation of mononuclear cells from whole blood samples, andpreparation of RNA from the mononuclear cells. In a preferredembodiment, the use of the RNA preparation tool is fully automated, sothat the cell separation and RNA isolation would require no humanmanipulations. Full automation is advantageous because it minimizesdelay, and standardizes sample preparation across differentlaboratories. This standardization increases the reproducibility of theresults.

FIG. 2 depicts the processes performed by the RNA preparation tool ofthe invention. A primary component of the device is a centrifuge (A).Tubes of whole blood containing a density gradient solution,transcription/translation inhibitors, and a gel barrier that separateserythrocytes from mononuclear cells and serum after centrifugation areplaced in the centrifuge (B). The barrier is permeable to erythrocytesand granulocytes during centrifugation, but does not allow mononuclearcells to pass through (or the barrier substance has a density such thatmononuclear cells remain above the level of the barrier during thecentrifugation). After centrifugation, the erythrocytes and granulocytesare trapped beneath the barrier, facilitating isolation of themononuclear cell and serum layers. A mechanical arm removes the tube andinverts it to mix the mononuclear cell layer and the serum (C). The armnext pours the supernatant into a fresh tube (D), while the erythrocytesand granulocytes remained below the barrier. Alternatively, a needle isused to aspirate the supernatant and transfer it to a fresh tube. Themechanical arms of the device opens and closes lids, dispenses PBS toaid in the collection of the mononuclear cells by centrifugation, andmoves the tubes in and out of the centrifuge. Following centrifugation,the supernatant is poured off or removed by a vacuum device (E), leavingan isolated mononuclear cell pellet. Purification of the RNA from thecells is performed automatically, with lysis buffer and otherpurification solutions (F) automatically dispensed and removed beforeand after centrifugation steps. The result is a purified RNA solution.In another embodiment, RNA isolation is performed using a column orfilter method. In yet another embodiment, the invention includes anon-board homogenizer for use in cell lysis.

Other Automated Systems

Automated and/or semi-automated methods for solid and liquid phasehigh-throughput sample preparation and evaluation are available, andsupported by commercially available devices. For example, roboticdevices for preparation of nucleic acids from bacterial colonies, e.g.,to facilitate production and characterization of the candidate libraryinclude, for example, an automated colony picker (e.g., the Q-bot,Genetix, U.K.) capable of identifying, sampling, and inoculating up to10,000/4 hrs different clones into 96 well microtiter dishes.Alternatively, or in addition, robotic systems for liquid handling areavailable from a variety of sources, e.g., automated workstations likethe automated synthesis apparatus developed by Takeda ChemicalIndustries, LTD. (Osaka, Japan) and many robotic systems utilizingrobotic arms (Zymate II, Zymark Corporation, Hopkinton, Mass.; Orca,Beckman Coulter, Inc. (Fullerton, Calif.)) which mimic the manualoperations performed by a scientist. Any of the above devices aresuitable for use with the present invention, e.g., for high-throughputanalysis of library components or subject leukocyte samples. The natureand implementation of modifications to these devices (if any) so thatthey can operate as discussed herein will be apparent to persons skilledin the relevant art.

High throughput screening systems that automate entire procedures, e.g.,sample and reagent pipetting, liquid dispensing, timed incubations, andfinal readings of the microplate in detector(s) appropriate for therelevant assay are commercially available. (see, e.g., Zymark Corp.,Hopkinton, Mass.; Air Technical Industries, Mentor, Ohio; BeckmanInstruments, Inc. Fullerton, Calif.; Precision Systems, Inc., Natick,Mass., etc.). These configurable systems provide high throughput andrapid start up as well as a high degree of flexibility andcustomization. Similarly, arrays and array readers are available, e.g.,from Affymetrix, PE Biosystems, and others.

The manufacturers of such systems provide detailed protocols the varioushigh throughput. Thus, for example, Zymark Corp. provides technicalbulletins describing screening systems for detecting the modulation ofgene transcription, ligand binding, and the like.

A variety of commercially available peripheral equipment, including,e.g., optical and fluorescent detectors, optical and fluorescentmicroscopes, plate readers, CCD arrays, phosphorimagers, scintillationcounters, phototubes, photodiodes, and the like, and software isavailable for digitizing, storing and analyzing a digitized video ordigitized optical or other assay results, e.g., using PC (Intel x86 orpentium chip-compatible DOS™, OS2™ WINDOWS™, WINDOWS NT™ or WINDOWS95™based machines), MACINTOSH™, or UNIX based (e.g., SUN™ work station)computers.

Embodiment in a Web Site

The methods described above can be implemented in a localized ordistributed computing environment. For example, if a localized computingenvironment is used, an array comprising a candidate nucleotide library,or diagnostic nucleotide set, is configured in proximity to a detector,which is, in turn, linked to a computational device equipped with userinput and output features.

In a distributed environment, the methods can be implemented on a singlecomputer with multiple processors or, alternatively, on multiplecomputers. The computers can be linked, e.g. through a shared bus, butmore commonly, the computer(s) are nodes on a network. The network canbe generalized or dedicated, at a local level or distributed over a widegeographic area. In certain embodiments, the computers are components ofan intra-net or an internet.

The predictive data corresponding to subject molecular signatures (e.g.,expression profiles, and related diagnostic, prognostic, or monitoringresults) can be shared by a variety of parties. In particular, suchinformation can be utilized by the subject, the subject's health carepractitioner or provider, a company or other institution, or ascientist. An individual subject's data, a subset of the database or theentire database recorded in a computer readable medium can be accesseddirectly by a user by any method of communication, including, but notlimited to, the internet. With appropriate computational devices,integrated systems, communications networks, users at remote locations,as well as users located in proximity to, e.g., at the same physicalfacility, the database can access the recorded information. Optionally,access to the database can be controlled using unique alphanumericpasswords that provide access to a subset of the data. Such provisionscan be used, e.g., to ensure privacy, anonymity, etc.

Typically, a client (e.g., a patient, practitioner, provider, scientist,or the like) executes a Web browser and is linked to a server computerexecuting a Web server. The Web browser is, for example, a program suchas IBM's Web Explorer, Internet explorer, NetScape or Mosaic, or thelike. The Web server is typically, but not necessarily, a program suchas IBM's HTTP Daemon or other WWW daemon (e.g., LINUX-based forms of theprogram). The client computer is bi-directionally coupled with theserver computer over a line or via a wireless system. In turn, theserver computer is bi-directionally coupled with a website (serverhosting the website) providing access to software implementing themethods of this invention.

A user of a client connected to the Intranet or Internet may cause theclient to request resources that are part of the web site(s) hosting theapplication(s) providing an implementation of the methods describedherein. Server program(s) then process the request to return thespecified resources (assuming they are currently available). A standardnaming convention has been adopted, known as a Uniform Resource Locator(“URL”). This convention encompasses several types of location names,presently including subclasses such as Hypertext Transport Protocol(“http”), File Transport Protocol (“ftp”), gopher, and Wide AreaInformation Service (“WAIS”). When a resource is downloaded, it mayinclude the URLs of additional resources. Thus, the user of the clientcan easily learn of the existence of new resources that he or she hadnot specifically requested.

Methods of implementing Intranet and/or Intranet embodiments ofcomputational and/or data access processes are well known to those ofskill in the art and are documented, e.g., in ACM Press, pp. 383-392;ISO-ANSI, Working Draft, “Information Technology-Database Language SQL”,Jim Melton, Editor, International Organization for Standardization andAmerican National Standards Institute, July 1992; ISO Working Draft,“Database Language SQL-Part 2:Foundation (SQL/Foundation)”,CD9075-2:199.chi.SQL, Sep. 11, 1997; and Cluer et al. (1992) A GeneralFramework for the Optimization of Object-Oriented Queries, Proc SIGMODInternational Conference on Management of Data, San Diego, Calif., Jun.2-5, 1992, SIGMOD Record, vol. 21, Issue 2, Jun., 1992; Stonebraker, M.,Editor. Other resources are available, e.g., from Microsoft, IBM, Sunand other software development companies.

Using the tools described above, users of the reagents, methods anddatabase as discovery or diagnostic tools can query a centrally locateddatabase with expression and subject data. Each submission of data addsto the sum of expression and subject information in the database. Asdata is added, a new correlation statistical analysis is automaticallyrun that incorporates the added clinical and expression data.Accordingly, the predictive accuracy and the types of correlations ofthe recorded molecular signatures increases as the database grows.

For example, subjects, such as patients, can access the results of theexpression analysis of their leukocyte samples and any accrued knowledgeregarding the likelihood of the patient's belonging to any specifieddiagnostic (or prognostic, or monitoring, or risk group), i.e., theirexpression profiles, and/or molecular signatures. Optionally, subjectscan add to the predictive accuracy of the database by providingadditional information to the database regarding diagnoses, testresults, clinical or other related events that have occurred since thetime of the expression profiling. Such information can be provided tothe database via any form of communication, including, but not limitedto, the internet. Such data can be used to continually define (andredefine) diagnostic groups. For example, if 1000 patients submit dataregarding the occurrence of myocardial infarction over the 5 years sincetheir expression profiling, and 300 of these patients report that theyhave experienced a myocardial infarction and 700 report that they havenot, then the 300 patients define a new “group A.” As the algorithm isused to continually query and revise the database, a new diagnosticnucleotide set that differentiates groups A and B (i.e., with andwithout myocardial infarction within a five year period) is identified.This newly defined nucleotide set is then be used (in the mannerdescribed above) as a test that predicts the occurrence of myocardialinfarction over a five-year period. While submission directly by thepatient is exemplified above, any individual with access and authorityto submit the relevant data e.g., the patient's physician, a laboratorytechnician, a health care or study administrator, or the like, can doso.

As will be apparent from the above examples, transmission of informationvia the internet (or via an intranet) is optionally bi-directional. Thatis, for example, data regarding expression profiles, subject data, andthe like are transmitted via a communication system to the database,while information regarding molecular signatures, predictive analysis,and the like, are transmitted from the database to the user. Forexample, using appropriate configurations of an integrated systemincluding a microarray comprising a diagnostic nucleotide set, adetector linked to a computational device can directly transmit (locallyor from a remote workstation at great distance, e.g., hundreds orthousands of miles distant from the database) expression profiles and acorresponding individual identifier to a central database for analysisaccording to the methods of the invention. According to, e.g., thealgorithms described above, the individual identifier is assigned to oneor more diagnostic (or prognostic, or monitoring, etc.) categories. Theresults of this classification are then relayed back, via, e.g., thesame mode of communication, to a recipient at the same or differentinternet (or intranet) address.

Kits

The present invention is optionally provided to a user as a kit.Typically, a kit contains one or more diagnostic nucleotide sets of theinvention. Alternatively, the kit contains the candidate nucleotidelibrary of the invention. Most often, the kit contains a diagnosticnucleotide probe set, or other subset of a candidate library, e.g., as acDNA or antibody microarray packaged in a suitable container. The kitmay further comprise, one or more additional reagents, e.g., substrates,labels, primers, for labeling expression products, tubes and/or otheraccessories, reagents for collecting blood samples, buffers, e.g.,erythrocyte lysis buffer, leukocyte lysis buffer, hybridizationchambers, cover slips, etc., as well as a software package, e.g.,including the statistical methods of the invention, e.g., as describedabove, and a password and/or account number for accessing the compileddatabase. The kit optionally further comprises an instruction set oruser manual detailing preferred methods of using the diagnosticnucleotide sets in the methods of the invention. In one embodiment, thekit may include contents useful for the discovery of diagnosticnucleotide sets using microarrays. The kit may include sterile,endotoxin and RNAse free blood collection tubes. The kit may alsoinclude alcohol swabs, tourniquet, blood collection set, and/or PBS(phosphate buffer saline; needed when method of example 2 is used toderived mononuclear RNA). The kit may also include cell lysis buffer.The kit may include RNA isolation kit, substrates for labeling of RNA(may vary for various expression profiling techniques). The kit may alsoinclude materials for fluorescence microarray expression profiling,including one or more of the following: reverse transcriptase and 10×RTbuffer, T7(dT)24 primer (primer with T7 promoter at 5′ end), DTT,deoxynucleotides, optionally 100 mM each, RNAse inhibitor, second strandcDNA buffer, DNA polymerase, Rnase H, T7 RNA polymerase ribonucleotides,in vitro transcription buffer, and/or Cy3 and Cy5 labeledribonucleotides. The kit may also include microarrays containingcandidate gene libraries, cover slips for slides, and/or hybridizationchambers. The kit may further include software package foridentification of diagnostic gene set from data, that containsstatistical methods, and/or allows alteration in desired sensitivity andspecificity of gene set. The software may further facilitate access toand data analysis by centrally a located database server. The softwaremay further include a password and account number to access centraldatabase server. In addition, the kit may include a kit user manual.

In another embodiment, the kit may include contents useful for theapplication of diagnostic nucleotide sets using microarrays. The kit mayinclude sterile, endotoxin and/or RNAse free blood collection tubes. Thekit may also include, alcohol swabs, tourniquet, and/or a bloodcollection set. The kit may further include PBS (phosphate buffersaline; needed when method of example 2 is used to derived mononuclearRNA), cell lysis buffer, and/or an RNA isolation kit. In addition, thekit may include substrates for labeling of RNA (may vary for variousexpression profiling techniques). For fluorescence microarray expressionprofiling, components may include reverse transcriptase and 10×RTbuffer, T7(dT)24 primer (primer with T7 promoter at 5′ end), DTT,deoxynucleotides (optionally 100 mM each), RNAse inhibitor, secondstrand cDNA buffer, DNA polymerase, Rnase H, T7 RNA polymerase,ribonucleotides, in vitro transcription buffer, and/or Cy3 and Cy5labeled ribonucleotides. The kit may further include microarrayscontaining candidate gene libraries. The kit may also include coverslips for slides, and/or hybridization chambers. The kit may include asoftware package for identification of diagnostic gene set from data.The software package may contain statistical methods, allow alterationin desired sensitivity and specificity of gene set, and/or facilitateaccess to and data analysis by centrally located database server. Thesoftware package may include a password and account number to accesscentral database server. In addition, the kit may include a kit usermanual.

In another embodiment, the kit may include contents useful for theapplication of diagnostic nucleotide sets using real-time PCR. This kitmay include terile, endotoxin and/or RNAse free blood collection tubes.The kit may further include alcohol swabs, tourniquet, and/or a bloodcollection set. The kit may also include PBS (phosphate buffer saline;needed when method of example 2 is used to derived mononuclear RNA). Inaddition, the kit may include cell lysis buffer and/or an RNA isolationkit. The kit may laso include substrates for real time RT-PCR, which mayvary for various real-time PCR techniques, including poly dT primers,random hexamer primers, reverse Transcriptase and RT buffer, DTT,deoxynucleotides 100 mM, RNase H, primer pairs for diagnostic andcontrol gene set, 10×PCR reaction buffer, and/or Taq DNA polymerase. Thekit may also include fluorescent probes for diagnostic and control geneset (alternatively, fluorescent dye that binds to only double strandedDNA). The kit may further include reaction tubes with or without barcodefor sample tracking, 96-well plates with barcode for sampleidentification, one barcode for entire set, or individual barcode perreaction tube in plate. The kit may also include a software package foridentification of diagnostic gene set from data, and/or statisticalmethods. The software package may allow alteration in desiredsensitivity and specificity of gene set, and/or facilitate access to anddata analysis by centrally located database server. The kit may includea password and account number to access central database server.Finally, the kit may include a kit user manual.

This invention will be better understood by reference to the followingnon-limiting Examples:

List of Example Titles

Example 1: Preparation of a leukocyte cDNA array comprising a candidategene library

Example 2: Preparation of RNA from mononuclear cells for expressionprofiling

Example 3: Preparation of Universal Control RNA for use in leukocyteexpression profiling

Example 4. RNA Labeling and hybridization to a leukocyte cDNA array ofcandidate nucleotide sequences.

Example 5: Clinical study for the Identification of diagnostic gene setsuseful in diagnosis and treatment of Cardiac allograft rejection

Example 6: Identification of diagnostic nucleotide sets for kidney andliver allograft rejection

Example 7: Identification of diagnostic nucleotide sets for diagnosis ofcytomegalovirus

Example 8: Design of oligonucleotide probes

Example 9: Production of an array of 8,000 spotted 50 meroligonucleotides.

Example 10: Identification of diagnostic nucleotide sets for diagnosisof Cardiac Allograft Rejection using microarrays

Example 11: Amplification, labeling, and hybridization of total RNA toan oligonucleotide microarray

Example 12: Real-time PCR validation of array expression results

Example 13: Real-time PCR expression markers of acute allograftrejection

Example 14: Identification of diagnostic nucleotide sets for diagnosisof Cardiac Allograft Rejection using microarrays

Example 15: Correlation and Classification Analysis

Example 16: Acute allograft rejection: biopsy tissue gene expressionprofiling

Example 17: Microarray and PCR gene expression panels for diagnosis andmonitoring of acute allograft rejection

Example 18: Assay sample preparation

Example 19: Allograft rejection diagnostic gene sequence analysis

Example 20: Detection of proteins expressed by diagnostic gene sequences

Example 21: Detecting changes in the rate of hematopoiesis

EXAMPLES Example 1 Preparation of a Leukocyte cDNA Array Comprising aCandidate Gene Library

Candidate genes and gene sequences for leukocyte expression profilingare identified through methods described elsewhere in this document.Candidate genes are used to obtain or design probes for peripheralleukocyte expression profiling in a variety of ways.

A cDNA microarray carrying 384 probes was constructed using sequencesselected from the initial candidate library. cDNAs is selected fromT-cell libraries, PBMC libraries and buffy coat libraries.

96-Well PCR

Plasmids are isolated in 96-well format and PCR was performed in 96-wellformat. A master mix is made that contain the reaction buffer, dNTPs,forward and reverse primer and DNA polymerase was made. 99 ul of themaster mix was aliquoted into 96-well plate. 1 ul of plasmid (1-2 ng/ul)of plasmid was added to the plate. The final reaction concentration was10 mM Tris pH 8.3, 3.5 mM MgCl2, 25 mM KCl, 0.4 mM dNTPs, 0.4 uM M13forward primer, 0.4 M13 reverse primer, and 10 U of Taq Gold (AppliedBiosystems). The PCR conditions were:

Step 1 95 C for 10 min

Step 2 95 C for 15 sec

Step 3 56 C for 30 sec

Step 4 72 C for 2 min 15 seconds

Step 5 go to Step 2 39 times

Step 6 72 C for 10 minutes

Step 7 4 C for ever.

PCR Purification

PCR purification is done in a 96-well format. The ArrayIt (TelechemInternational, Inc.) PCR purification kit is used and the providedprotocol was followed without modification. Before the sample isevaporated to dryness, the concentration of PCR products was determinedusing a spectrophotometer. After evaporation, the samples arere-suspended in 1× Micro Spotting Solution (ArrayIt) so that themajority of the samples were between 0.2-1.0 ug/ul.

Array Fabrication

Spotted cDNA microarrays are then made from these PCR products byArrayIt using their protocols, which may be found at the ArrayItwebsite. Each fragment was spotted 3 times onto each array. Candidategenes and gene sequences for leukocyte expression profiling areidentified through methods described elsewhere in this document. Thosecandidate genes are used for peripheral leukocyte expression profiling.The candidate libraries can used to obtain or design probes forexpression profiling in a variety of ways.

Oligonucleotide probes are prepared using the gene sequences of Table 2,Table 8, and the sequence listing. Oligo probes are designed on acontract basis by various companies (for example, Compugen, Mergen,Affymnetrix, Telechem), or designed from the candidate sequences using avariety of parameters and algorithms as indicated at located at the MITweb site. Briefly, the length of the oligonucleotide to be synthesizedis determined, preferably greater than 18 nucleotides, generally 18-24nucleotides, 24-70 nucleotides and, in some circumstances, more than 70nucleotides. The sequence analysis algorithms and tools described aboveare applied to the sequences to mask repetitive elements, vectorsequences and low complexity sequences. Oligonucleotides are selectedthat are specific to the candidate nucleotide sequence (based on a Blastn search of the oligonucleotide sequence in question against genesequences databases, such as the Human Genome Sequence, UniGene, dbESTor the non-redundant database at NCBI), and have <50% G content and25-70% G+C content. Desired oligonucleotides are synthesized usingwell-known methods and apparatus, or ordered from a company (for exampleSigma). Oligonucleotides are spotted onto microarrays. Alternatively,oligonucleotides are synthesized directly on the array surface, using avariety of techniques (Hughes et al. 2001, Yershov et al. 1996, Lockhartet al 1996).

Example 2 Preparation of RNA from Mononuclear Cells for ExpressionProfiling

Blood was isolated from the subject for leukocyte expression profilingusing the following methods: Two tubes were drawn per patient. Blood wasdrawn from either a standard peripheral venous blood draw or directlyfrom a large-bore intra-arterial or intravenous catheter inserted in thefemoral artery, femoral vein, subclavian vein or internal jugular vein.Care was taken to avoid sample contamination with heparin from theintravascular catheters, as heparin can interfere with subsequent RNAreactions. For each tube, 8 ml of whole blood was drawn into a tube(CPT, Becton-Dickinson order #362753) containing the anticoagulantCitrate, 25° C. density gradient solution (e.g. Ficoll, Percoll) and apolyester gel barrier that upon centrifugation was permeable to RBCs andgranulocytes but not to mononuclear cells. The tube was inverted severaltimes to mix the blood with the anticoagulant. The tubes werecentrifuged at 1750×g in a swing-out rotor at room temperature for 20minutes. The tubes were removed from the centrifuge and inverted 5-10times to mix the plasma with the mononuclear cells, while trapping theRBCs and the granulocytes beneath the gel barrier. Theplasma/mononuclear cell mix was decanted into a 15 ml tube and 5 ml ofphosphate-buffered saline (PBS) is added. The 15 ml tubes were spun for5 minutes at 1750×g to pellet the cells. The supernatant was discardedand 1.8 ml of RLT lysis buffer is added to the mononuclear cell pellet.The buffer and cells were pipetted up and down to ensure complete lysisof the pellet. The cell lysate was frozen and stored until it isconvenient to proceed with isolation of total RNA.

Total RNA was purified from the lysed mononuclear cells using the QiagenRneasy Miniprep kit, as directed by the manufacturer (10/99 version) fortotal RNA isolation, including homogenization (Qiashredder columns) andon-column DNase treatment. The purified RNA was eluted in 50 ul ofwater. The further use of RNA prepared by this method is described inExamples 10 and 11.

Some samples were prepared by a different protocol, as follows:

Two 8 ml blood samples were drawn from a peripheral vein into a tube(CPT, Becton-Dickinson order #362753) containing anticoagulant(Citrate), 25° C. density gradient solution (Ficoll) and a polyester gelbarrier that upon centrifugation is permeable to RBCs and granulocytesbut not to mononuclear cells. The mononuclear cells and plasma remainedabove the barrier while the RBCs and granulocytes were trapped below.The tube was inverted several times to mix the blood with theanticoagulant, and the tubes were subjected to centrifugation at 1750×gin a swing-out rotor at room temperature for 20 min. The tubes wereremoved from the centrifuge, and the clear plasma layer above the cloudymononuclear cell layer was aspirated and discarded. The cloudymononuclear cell layer was aspirated, with care taken to rinse all ofthe mononuclear cells from the surface of the gel barrier with PBS(phosphate buffered saline). Approximately 2 mls of mononuclear cellsuspension was transferred to a 2 ml microcentrifuge tube, andcentrifuged for 3 min. at 16,000 rpm in a microcentrifuge to pellet thecells. The supernatant was discarded and 1.8 ml of RLT lysis buffer(Qiagen) were added to the mononuclear cell pellet, which lysed thecells and inactivated Rnases. The cells and lysis buffer were pipettedup and down to ensure complete lysis of the pellet. Cell lysate wasfrozen and stored until it was convenient to proceed with isolation oftotal RNA.

RNA samples were isolated from 8 mL of whole blood. Yields ranged from 2ug to 20 ug total RNA for 8 mL blood. A260/A280 spectrophotometricratios were between 1.6 and 2.0, indicating purity of sample. 2 ul ofeach sample were run on an agarose gel in the presence of ethidiumbromide. No degradation of the RNA sample and no DNA contamination wasvisible.

In some cases, specific subsets of mononuclear cells were isolated fromperipheral blood of human subjects. When this was done, the StemSep cellseparation kits (manual version 6.0.0) were used from StemCellTechnologies (Vancouver, Canada). This same protocol can be applied tothe isolation of T cells, CD4 T cells, CD8 T cells, B cells, monocytes,NK cells and other cells. Isolation of cell types using negativeselection with antibodies may be desirable to avoid activation of targetcells by antibodies.

Example 3 Preparation of Universal Control RNA for Use in LeukocyteExpression Profiling

Control RNA was prepared using total RNA from Buffy coats and/or totalRNA from enriched mononuclear cells isolated from Buffy coats, both withand without stimulation with ionomycin and PMA. The following controlRNAs were prepared:

Control 1: Buffy Coat Total RNA

Control 2: Mononuclear cell Total RNA

Control 3: Stimulated buffy coat Total RNA

Control 4: Stimulated mononuclear Total RNA

Control 5: 50% Buffy coat Total RNA/50% Stimulated buffy coat Total RNA

Control 6: 50% Mononuclear cell Total RNA/50% Stimulated MononuclearTotal RNA

Some samples were prepared using the following protocol: Buffy coatsfrom 38 individuals were obtained from Stanford Blood Center. Each buffycoat is derived from ˜350 mL whole blood from one individual. 10 mlbuffy coat was removed from the bag, and placed into a 50 ml tube. 40 mlof Buffer EL (Qiagen) was added, the tube was mixed and placed on icefor 15 minutes, then cells were pelleted by centrifugation at 2000×g for10 minutes at 4° C. The supernatant was decanted and the cell pellet wasre-suspended in 10 ml of Qiagen Buffer EL. The tube was then centrifugedat 2000×g for 10 minutes at 4° C. The cell pellet was then re-suspendedin 20 ml TRIZOL (GibcoBRL) per Buffy coat sample, the mixture wasshredded using a rotary homogenizer, and the lysate was then frozen at−80° C. prior to proceeding to RNA isolation.

Other control RNAs were prepared from enriched mononuclear cellsprepared from Buffy coats. Buffy coats from Stanford Blood Center wereobtained, as described above. 10 ml buffy coat was added to a 50 mlpolypropylene tube, and 10 ml of phosphate buffer saline (PBS) was addedto each tube. A polysucrose (5.7 g/dL) and sodium diatrizoate (9.0 g/dL)solution at a 1.077+/−0.0001 g/ml density solution of equal volume todiluted sample was prepared (Histopaque 1077, Sigma cat. no 1077-1).This and all subsequent steps were performed at room temperature. 15 mlof diluted buffy coat/PBS was layered on top of 15 ml of the histopaquesolution in a 50 ml tube. The tube was centrifuged at 400×g for 30minutes at room temperature. After centrifugation, the upper layer ofthe solution to within 0.5 cm of the opaque interface containing themononuclear cells was discarded. The opaque interface was transferredinto a clean centrifuge tube. An equal volume of PBS was added to eachtube and centrifuged at 350×g for 10 minutes at room temperature. Thesupernatant was discarded. 5 ml of Buffer EL (Qiagen) was used toresuspend the remaining cell pellet and the tube was centrifuged at2000×g for 10 minutes at room temperature. The supernatant wasdiscarded. The pellet was resuspended in 20 ml of TRIZOL (GibcoBRL) foreach individual buffy coat that was processed. The sample washomogenized using a rotary homogenizer and frozen at −80 C until RNA wasisolated. RNA was isolated from frozen lysed Buffy coat samples asfollows: frozen samples were thawed, and 4 ml of chloroform was added toeach buffy coat sample. The sample was mixed by vortexing andcentrifuged at 2000×g for 5 minutes. The aqueous layer was moved to newtube and then repurified by using the RNeasy Maxi RNA clean up kit,according to the manufacturer's instruction (Qiagen, PN 75162). Theyield, purity and integrity were assessed by spectrophotometer and gelelectrophoresis. Some samples were prepared by a different protocol, asfollows. The further use of RNA prepared using this protocol isdescribed in Example 11.

50 whole blood samples were randomly selected from consented blooddonors at the Stanford Medical School Blood Center. Each huffy coatsample was produced from ˜350 mL of an individual's donated blood. Thewhole blood sample was centrifuged at ˜4,400×g for 8 minutes at roomtemperature, resulting in three distinct layers: a top layer of plasma,a second layer of buffy coat, and a third layer of red blood cells. 25ml of the buffy coat fraction was obtained and diluted with an equalvolume of PBS (phosphate buffered saline). 30 ml of diluted huffy coatwas layered onto 15 ml of sodium diatrizoate solution adjusted to adensity of 1.077+/−0.001 g/ml (Histopaque 1077, Sigma) in a 50 mLplastic tube. The tube was spun at 800 g for 10 minutes at roomtemperature. The plasma layer was removed to the 30 ml mark on the tube,and the mononuclear cell layer removed into a new tube and washed withan equal volume of PBS, and collected by centrifugation at 2000 g for 10minutes at room temperature. The cell pellet was resuspended in 10 ml ofBuffer EL (Qiagen) by vortexing and incubated on ice for 10 minutes toremove any remaining erthythrocytes. The mononuclear cells were spun at2000 g for 10 minutes at 4 degrees Celsius. The cell pellet was lysed in25 ml of a phenol/guanidinium thiocyanate solution (TRIZOL Reagent,Invitrogen). The sample was homogenized using a PowerGene 5 rotaryhomogenizer (Fisher Scientific) and Omini disposable generator probes(Fisher Scientific). The Trizol lysate was frozen at −80 degrees C.until the next step.

The samples were thawed out and incubated at room temperature for 5minutes. 5 ml chloroform was added to each sample, mixed by vortexing,and incubated at room temperature for 3 minutes. The aqueous layers weretransferred to new 50 ml tubes. The aqueous layer containing total RNAwas further purified using the Qiagen RNeasy Maxi kit (PN 75162), perthe manufacturer's protocol (October 1999). The columns were elutedtwice with 1 ml Rnase-free water, with a minute incubation before eachspin. Quantity and quality of RNA was assessed using standard methods.Generally, RNA was isolated from batches of 10 buffy coats at a time,with an average yield per buffy coat of 870 μg, and an estimated totalyield of 43.5 mg total RNA with a 260/280 ratio of 1.56 and a 28S/18Sratio of 1.78.

Quality of the RNA was tested using the Agilent 2100 Bioanalyzer usingRNA 6000 microfluidics chips. Analysis of the electrophorgrams from theBioanalyzer for five different batches demonstrated the reproducibilityin quality between the batches.

Total RNA from all five batches were combined and mixed in a 50 ml tube,then aliquoted as follows: 2×10 ml aliquots in 15 ml tubes, and the restin 100 μl aliquots in 1.5 ml microcentrifuge tubes. The aliquots gavehighly reproducible results with respect to RNA purity, size andintegrity. The RNA was stored at −80° C.

Test Hybridization of Reference RNA.

When compared with BC38 and Stimulated mononuclear reference samples,the R50 performed as well, if not better than the other referencesamples as shown in FIG. 3. In an analysis of hybridizations, where theR50 targets were fluorescently labeled with Cy-5 using methods describedherein and the amplified and labeled aRNA was hybridized (as in example11) to the olignucleotide array described in example 9. The R50 detected97.3% of probes with a Signal to Noise ratio (S/N) of greater than threeand 99.9% of probes with S/N greater than one.

Example 4 RNA Labeling and Hybridization to a Leukocyte cDNA Array ofCandidate Nucleotide Sequences

Comparison of Guanine-Silica to Acid-Phenol RNA Purification (GSvsAP)

These data are from a set of 12 hybridizations designed to identifydifferences between the signal strength from two different RNApurification methods. The two RNA methods used were guanidine-silica(GS, Qiagen) and acid-phenol (AP, Trizol, Gibco BRL). Ten tubes of bloodwere drawn from each of four people. Two were used for the AP prep, theother eight were used for the GS prep. The protocols for the leukocyteRNA preps using the AP and GS techniques were completed as describedhere:

Guanidine-Silica (GS) Method:

For each tube, 8 ml blood was drawn into a tube containing theanticoagulant Citrate, 25° C. density gradient solution and a polyestergel barrier that upon centrifugation is permeable to RBCs andgranulocytes but not to mononuclear cells. The mononuclear cells andplasma remained above the barrier while the RBCs and granulocytes weretrapped below. CPT tubes from Becton-Dickinson (#362753) were used forthis purpose. The tube was inverted several times to mix the blood withthe anticoagulant. The tubes were immediately centrifuged @1750×g in aswinging bucket rotor at room temperature for 20 min. The tubes wereremoved from the centrifuge and inverted 5-10 times. This mixed theplasma with the mononuclear cells, while the RBCs and the granulocytesremained trapped beneath the gel barrier. The plasma/mononuclear cellmix was decanted into a 15 ml tube and 5 ml of phosphate-buffered saline(PBS) was added. The 15 ml tubes are spun for 5 minutes at 1750×g topellet the cells. The supernatant was discarded and 1.8 ml of RLT lysisbuffer (guanidine isothyocyanate) was added to the mononuclear cellpellet. The buffer and cells were pipetted up and down to ensurecomplete lysis of the pellet. The cell lysate was then processed exactlyas described in the Qiagen Rneasy Miniprep kit protocol (10/99 version)for total RNA isolation (including steps for homogenization (Qiashreddercolumns) and on-column DNase treatment. The purified RNA was eluted in50 ul of water.

Acid-Phenol (AP) Method:

For each tube, 8 ml blood was drawn into a tube containing theanticoagulant Citrate, 25° C. density gradient solution and a polyestergel barrier that upon centrifugation is permeable to RBCs andgranulocytes but not to mononuclear cells. The mononuclear cells andplasma remained above the barrier while the RBCs and granulocytes weretrapped below. CPT tubes from Becton-Dickinson (#362753) were used forthis purpose. The tube was inverted several times to mix the blood withthe anticoagulant. The tubes were immediately centrifuged @1750×g in aswinging bucket rotor at room temperature for 20 min. The tubes wereremoved from the centrifuge and inverted 5-10 times. This mixed theplasma with the mononuclear cells, while the RBCs and the granulocytesremained trapped beneath the gel barrier. The plasma/mononuclear cellmix was decanted into a 15 ml tube and 5 ml of phosphate-buffered saline(PBS) was added. The 15 ml tubes are spun for 5 minutes @1750×g topellet the cells. The supernatant was discarded and the cell pellet waslysed using 0.6 mL Phenol/guanidine isothyocyanate (e.g. Trizol reagent,GibcoBRL). Subsequent total RNA isolation proceeded using themanufacturers protocol.

RNA from each person was labeled with either Cy3 or Cy5, and thenhybridized in pairs to the mini-array. For instance, the first array washybridized with GS RNA from one person (Cy3) and GS RNA from a secondperson (Cy5).

Techniques for labeling and hybridization for all experiments discussedhere were completed as detailed above. Arrays were prepared as describedin example 1.

RNA isolated from subject samples, or control Buffy coat RNA, werelabeled for hybridization to a cDNA array. Total RNA (up to 100 μg) wascombined with 2 μl of 100 μM solution of an Oligo (dT)12-18 (GibcoBRL)and heated to 70° C. for 10 minutes and place on ice. Reaction bufferwas added to the tube, to a final concentration of 1×RT buffer(GibcoBRL), 10 mM DTT (GibcoBRL), 0.1 mM unlabeled dATP, dTTP, and dGTP,and 0.025 mM unlabeled dCTP, 200 pg of CAB (A. thaliana photosystem Ichlorophyll a/b binding protein), 200 pg of RCA (A. thaliana RUBISCOactivase), 0.25 mM of Cy-3 or Cy-5 dCTP, and 400 U Superscript II RT(GibcoBRL).

The volumes of each component of the labeling reaction were as follows:20 μl of 5×RT buffer; 10 μl of 100 mM DTT; 1 μl of 10 mM dNTPs withoutdCTP; 0.5 μl of 5 mM CTP; 13 μl of H20; 0.02 μl of 10 ng/μl CAB and RCA;1 μl of 40 Units/μl RNAseOUT Recombinant Ribonuclease Inhibitor(GibcoBRL); 2.5 μl of 1.0 mM Cy-3 or Cy-5 dCTP; and 2.0 μl of 200Units/∞l of Superscript II RT. The sample was vortexed and centrifuged.The sample was incubated at 4° C. for 1 hour for first strand cDNAsynthesis, then heated at 70° C. for 10 minutes to quench enzymaticactivity. 1 μl of 10 mg/ml of Rnase A was added to degrade the RNAstrand, and the sample was incubated at 37° C. for 30 minutes. Next, theCy-3 and Cy-5 cDNA samples were combined into one tube. Unincorporatednucleotides were removed using QIAquick RCR purification protocol(Qiagen), as directed by the manufacturer. The sample was evaporated todryness and resuspended in 5 μl of water. The sample was mixed withhybridization buffer containing 5×SSC, 0.2% SDS, 2 mg/ml Cot-1 DNA(GibcoBRL), 1 mg/ml yeast tRNA (GibcoBRL), and 1.6 ng/μl poly dA40-60(Pharmacia). This mixture was placed on the microarray surface and aglass cover slip was placed on the array (Corning). The microarray glassslide was placed into a hybridization chamber (ArrayIt). The chamber wasthen submerged in a water bath overnight at 62° C. The microarray wasremoved from the cassette and the cover slip was removed by repeatedlysubmerging it to a wash buffer containing 1×SSC, and 0.1% SDS. Themicroarray slide was washed in 1×SSC/0.1% SDS for 5 minutes. The slidewas then washed in 0.1% SSC/0.1% SDS for 5 minutes. The slide wasfinally washed in 0.1×SSC for 2 minutes. The slide was spun at 1000 rpmfor 2 minutes to dry out the slide, then scanned on a microarray scanner(Axon Instruments, Union City, Calif.).

Six hybridizations with 20 μg of RNA were performed for each type of RNApreparation (GS or AP). Since both the Cy3 and the Cy5 labeled RNA arefrom test preparations, there are six data points for each GS prepped,Cy3-labeled RNA and six for each GS-prepped, Cy5-labeled RNA. The miniarray hybridizations were scanned on and Axon Instruments scanner usingGenPix 3.0 software. The data presented were derived as follows. First,all features flagged as “not found” by the software were removed fromthe dataset for individual hybridizations. These features are usuallydue to high local background or other processing artifacts. Second, themedian fluorescence intensity minus the background fluorescenceintensity was used to calculate the mean background subtracted signalfor each dye for each hybridization. In FIG. 3, the mean of these meansacross all six hybridizations is graphed (n=6 for each column). Theerror bars are the SEM. This experiment shows that the average signalfrom AP prepared RNA is 47% of the average signal from GS prepared RNAfor both Cy3 and Cy5.

Generation of Expression Data for Leukocyte Genes from PeripheralLeukocyte Samples

Six hybridizations were performed with RNA purified from human bloodleukocytes using the protocols given above. Four of the six wereprepared using the GS method and 2 were prepared using the AP method.Each preparation of leukocyte RNA was labeled with Cy3 and 10 μghybridized to the mini-array. A control RNA was batch labeled with Cy5and 10 μg hybridized to each mini-array together with the Cy3-labeledexperimental RNA.

The control RNA used for these experiments was Control 1: Buffy CoatRNA, as described above.

The protocol for the preparation of that RNA is reproduced here:

Buffy Coat RNA Isolation:

Buffy coats were obtained from Stanford Blood Center (in total 38individual buffy coats were used. Each buffy coat is derived from ˜350mL whole blood from one individual. 10 ml buffy coat was taken andplaced into a 50 ml tube and 40 ml of a hypochlorous acid (HOCl)solution (Buffer EL from Qiagen) was added. The tube was mixed andplaced on ice for 15 minutes. The tube was then centrifuged at 2000×gfor 10 minutes at 4° C. The supernatant was decanted and the cell pelletwas re-suspended in 10 ml of hypochlorous acid solution (Qiagen BufferEL). The tube was then centrifuged at 2000×g for 10 minutes at 4° C. Thecell pellet was then re-suspended in 20 ml phenol/guanidine thiocyanatesolution (TRIZOL from GibcoBRL) for each individual buffy coat that wasprocessed. The mixture was then shredded using a rotary homogenizer. Thelysate was then frozen at −80° C. prior to proceeding to RNA isolation.

The arrays were then scanned and analyzed on an Axon Instruments scannerusing GenePix 3.0 software. The data presented were derived as follows.First, all features flagged as “not found” by the software were removedfrom the dataset for individual hybridizations. Second, control featureswere used to normalize the data for labeling and hybridizationvariability within the experiment. The control features are cDNA forgenes from the plant, Arabidopsis thaliana, that were included whenspotting the mini-array. Equal amounts of RNA complementary to two ofthese cDNAs were added to each of the samples before they were labeled.A third was pre-labeled and equal amounts were added to eachhybridization solution before hybridization. Using the signal from thesegenes, we derived a normalization constant (L_(j)) according to thefollowing formula:

$L_{j} = \frac{\frac{\sum\limits_{i = 1}^{N}\;{BGSS}_{j,i}}{N}}{\frac{\sum\limits_{j = 1}^{K}\;\frac{\sum\limits_{i = 1}^{N}\;{BGSS}_{j,i}}{N}}{K}}$where BGSS_(i) is the signal for a specific feature as identified in theGenePix software as the median background subtracted signal for thatfeature, N is the number of A. thaliana control features, K is thenumber of hybridizations, and L is the normalization constant for eachindividual hybridization. Using the formula above, the mean over allcontrol features of a particular hybridization and dye (e.g. Cy3) wascalculated. Then these control feature means for all Cy3 hybridizationswere averaged. The control feature mean in one hybridization divided bythe average of all hybridizations gives a normalization constant forthat particular Cy3 hybridization.

The same normalization steps were performed for Cy3 and Cy5 values, bothfluorescence and background. Once normalized, the background Cy3fluorescence was subtracted from the Cy3 fluorescence for each feature.Values less than 100 were eliminated from further calculations since lowvalues caused spurious results.

FIG. 4 shows the average background subtracted signal for each of nineleukocyte-specific genes on the mini array. This average is for 3-6 ofthe above-described hybridizations for each gene. The error bars are theSEM.

The ratio of Cy3 to Cy5 signal is shown for a number of genes. Thisratio corrects for variability among hybridizations and allowscomparison between experiments done at different times. The ratio iscalculated as the Cy3 background subtracted signal divided by the Cy5background subtracted signal. Each bar is the average for 3-6hybridizations. The error bars are SEM.

Together, these results show that we can measure expression levels forgenes that are expressed specifically in sub-populations of leukocytes.These expression measurements were made with only 10 μg of leukocytetotal RNA that was labeled directly by reverse transcription. The signalstrength can be increased by improved labeling techniques that amplifyeither the starting RNA or the signal fluorescence. In addition,scanning techniques with higher sensitivity can be used.

Genes in FIGS. 4 and 5:

GenBank Accession Gene Name Gene Name/Description Number Abbreviation Tcell-specific tyrosine kinase Mrna L10717 TKTCS Interleukin 1 alpha(IL 1) mRNA, complete NM_000575 IL1A cds T-cell surface antigen CD2(T11) mRNA, M14362 CD2 complete cds Interleukin-13 (IL-13) precursorgene, U31120 IL-13 complete cds Thymocyte antigen CD1a mRNA, completeM28825 CD1a cds CD6 mRNA for T cell glycoprotein CDS NM_006725 CD6 MHCclass II HLA-DQA1 mRNA, complete U77589 HLA-DQA1 cds Granulocytecolony-stimulating factor M28170 CD19 Homo sapiens CD69 antigenNM_001781 CD69

Example 5 Clinical Study to Identify Diagnostic Gene Sets Useful inDiagnosis and Treatment of Cardiac Allograft Recipients

An observational study was conducted in which a prospective cohort ofcardiac transplant recipients were analyzed for associations betweenclinical events or rejection grades and expression of a leukocytecandidate nucleotide sequence library. Patients were identified at 4cardiac transplantation centers while on the transplant waiting list orduring their routing post-transplant care. All adult cardiac transplantrecipients (new or re-transplants) who received an organ at the studycenter during the study period or within 3 months of the start of thestudy period were eligible. The first year after transplantation is thetime when most acute rejection occurs and it is thus important to studypatients during this period. Patients provided informed consent prior tostudy procedures.

Peripheral blood leukocyte samples were obtained from all patients atthe following time points: prior to transplant surgery (when able), thesame day as routinely scheduled screening biopsies, upon evaluation forsuspected acute rejection (urgent biopsies), on hospitalization for anacute complication of transplantation or immunosuppression, and whenCytomegalovirus (CMV) infection was suspected or confirmed. Samples wereobtained through a standard peripheral vein blood draw or through acatheter placed for patient care (for example, a central venous catheterplaced for endocardial biopsy). When blood was drawn from a intravenousline, care was taken to avoid obtaining heparin with the sample as itcan interfere with downstream reactions involving the RNA. Mononuclearcells were prepared from whole blood samples as described in Example 2.Samples were processed within 2 hours of the blood draw and DNA andserum were saved in addition to RNA. Samples were stored at −80° C. oron dry ice and sent to the site of RNA preparation in a sealed containerwith ample dry ice. RNA was isolated from subject samples as describedin Example 2 and hybridized to a candidate library of differentiallyexpressed leukocyte nucleotide sequences, as further described inExamples 9-10. Methods used for amplification, labeling, hybridizationand scanning are described in Example 11. Analysis of human transplantpatient mononuclear cell RNA hybridized to a microarray andidentification of diagnostic gene sets is shown in Example 10.

From each patient, clinical information was obtained at the followingtime points: prior to transplant surgery (when available), the same dayas routinely scheduled screening biopsies, upon evaluation for suspectedacute rejection (e.g., urgent biopsies), on hospitalization for an acutecomplication of transplantation or immunosuppression, and whenCytomegalovirus (CMV) infection was suspected or confirmed. Data wascollected directly from the patient, from the patient's medical record,from diagnostic test reports or from computerized hospital databases. Itwas important to collect all information pertaining to the studyclinical correlates (diagnoses and patient events and states to whichexpression data is correlated) and confounding variables (diagnoses andpatient events and states that may result in altered leukocyte geneexpression. Examples of clinical data collected are: patient sex, dateof birth, date of transplant, race, requirement for prospective crossmatch, occurrence of pre-transplant diagnoses and complications,indication for transplantation, severity and type of heart disease,history of left ventricular assist devices, all known medical diagnoses,blood type, HLA type, viral serologies (including CMV, Hepatitis B andC, HIV and others), serum chemistries, white and red blood cell countsand differentials, CMV infections (clinical manifestations and methodsof diagnosis), occurrence of new cancer, hemodynamic parameters measuredby catheterization of the right or left heart (measures of graftfunction), results of echocardiography, results of coronary angiograms,results of intravascular ultrasound studies (diagnosis of transplantvasculopathy), medications, changes in medications, treatments forrejection, and medication levels. Information was also collectedregarding the organ donor, including demographics, blood type, HLA type,results of screening cultures, results of viral serologies, primarycause of brain death, the need for inotropic support, and the organ coldischemia time.

Of great importance was the collection of the results of endocardialbiopsy for each of the patients at each visit. Biopsy results were allinterpreted and recorded using the international society for heart andlung transplantation (ISHLT) criteria, described below. Biopsypathological grades were determined by experienced pathologists at eachcenter.

ISHLT Criteria

Rejection Grade Finding Severity 0 No lymphocytic infiltrates None 1AFocal (perivascular or interstitial lymphocytic Borderline infiltrateswithout necrosis) mild 1B Diffuse but sparse lymphocytic infiltrateswithout Mild necrosis 2 One focus only with aggressive lymphocytic Mild,focal infiltrate and/or myocyte damage moderate 3A Multifocal aggressivelymphocytic infiltrates Moderate and/or myocardial damage 3B Diffuseinflammatory lymphocytic infiltrates with Borderline necrosis Severe 4Diffuse aggressive polymorphous lymphocytic Severe infiltrates withedema hemorrhage and vasculitis, with necrosis

Because variability exists in the assignment of ISHLT grades, it wasimportant to have a centralized and blinded reading of the biopsy slidesby a single pathologist. This was arranged for all biopsy slidesassociated with samples in the analysis. Slides were obtained andassigned an encoded number. A single pathologist then read all slidesfrom all centers and assigned an ISHLT grade. Grades from the singlepathologist were then compared to the original grades derived from thepathologists at the study centers. For the purposes of correlationanalysis of leukocyte gene expression to biopsy grades, the centralizedreading information was used in a variety of ways (see Example 10 formore detail). In some analyses, only the original reading was used as anoutcome. In other analyses, the result from the centralized reader wasused as an outcome. In other analyses, the highest of the 2 grades wasused. For example, if the original assigned grade was 0 and thecentralized reader assigned a 1A, then 1A was the grade used as anoutcome. In some analyses, the highest grade was used and then samplesassociated with a Grade 1A reading were excluded from the analysis. Insome analyses, only grades with no disagreement between the 2 readingswere used as outcomes for correlation analysis. Clinical data wasentered and stored in a database. The database was queried to identifyall patients and patient visits that meet desired criteria (for example,patients with >grade II biopsy results, no CMV infection and time sincetransplant <12 weeks).

The collected clinical data (disease criteria) is used to define patientor sample groups for correlation of expression data. Patient groups areidentified for comparison, for example, a patient group that possesses auseful or interesting clinical distinction, versus a patient group thatdoes not possess the distinction. Examples of useful and interestingpatient distinctions that can be made on the basis of collected clinicaldata are listed here:

1. Rejection episode of at least moderate histologic grade, whichresults in treatment of the patient with additional corticosteroids,anti-T cell antibodies, or total lymphoid irradiation.

2. Rejection with histologic grade 2 or higher.

3. Rejection with histologic grade <2.

4. The absence of histologic rejection and normal or unchanged allograftfunction (based on hemodynamic measurements from catheterization or onechocardiographic data).

5. The presence of severe allograft dysfunction or worsening allograftdysfunction during the study period (based on hemodynamic measurementsfrom catheterization or on echocardiographic data).

6. Documented CMV infection by culture, histology, or PCR, and at leastone clinical sign or symptom of infection.

7. Specific graft biopsy rejection grades

8. Rejection of mild to moderate histologic severity promptingaugmentation of the patient's chronic immunosuppressive regimen

9. Rejection of mild to moderate severity with allograft dysfunctionprompting plasmaphoresis or a diagnosis of “humoral” rejection

10. Infections other than CMV, esp. Epstein Barr virus (EBV)

11. Lymphoproliferative disorder (also called, post-transplant lymphoma)

12. Transplant vasculopathy diagnosed by increased intimal thickness onintravascular ultrasound (IVUS), angiography, or acute myocardialinfarction.

13. Graft Failure or Retransplantation

14. All cause mortality

15. Grade 1A or higher rejection as defined by the initial biopsyreading.

16. Grade 1B or higher rejection as defined by the initial biopsyreading.

17. Grade 1A or higher rejection as defined by the centralized biopsyreading.

18. Grade 1B or higher rejection as defined by the centralized biopsyreading.

19. Grade 1A or higher rejection as defined by the highest of theinitial and centralized biopsy reading.

20. Grade 1B or higher rejection as defined by the highest of theinitial and centralized biopsy reading.

21. Any rejection >Grade 2 occurring in patient at any time in thepost-transplant course.

Expression profiles of subject samples are examined to discover sets ofnucleotide sequences with differential expression between patientgroups, for example, by methods describes above and below. Non-limitingexamples of patient leukocyte samples to obtain for discovery of variousdiagnostic nucleotide sets are as follows:Leukocyte set to avoid biopsy or select for biopsy:Samples: Grade 0 vs. Grades 1-4Leukocyte set to monitor therapeutic response:Examine successful vs. unsuccessful drug treatment.Samples:Successful: Time 1: rejection, Time 2: drug therapy Time 3: no rejection

-   -   Unsuccessful: Time 1: rejection, Time 2: drug therapy; Time 3:        rejection        Leukocyte set to predict subsequent acute rejection.        Biopsy may show no rejection, but the patient may develop        rejection shortly thereafter. Look at profiles of patients who        subsequently do and do not develop rejection.        Samples:        Group 1 (Subsequent rejection): Time 1: Grade 0; Time 2: Grade>0        Group 2 (No subsequent rejection): Time 1: Grade 0; Time 2:        Grade 0        Focal rejection may be missed by biopsy. When this occurs the        patient may have a Grade 0, but actually has rejection. These        patients may go on to have damage to the graft etc.        Samples:        Non-rejectors: no rejection over some period of time        Rejectors: an episode of rejection over same period        Leukocyte set to diagnose subsequent or current graft failure:        Samples:        Echocardiographic or catheterization data to define worsening        function over time and correlate to profiles.        Leukocyte set to diagnose impending active CMV:        Samples:        Look at patients who are CMV IgG positive. Compare patients with        subsequent (to a sample) clinical CMV infection verses no        subsequent clinical CMV infection.        Leukocyte set to diagnose current active CMV:        Samples:        Analyze patients who are CMV IgG positive. Compare patients with        active current clinical CMV infection vs. no active current CMV        infection.

Upon identification of a nucleotide sequence or set of nucleotidesequences that distinguish patient groups with a high degree ofaccuracy, that nucleotide sequence or set of nucleotide sequences isvalidated, and implemented as a diagnostic test. The use of the testdepends on the patient groups that are used to discover the nucleotideset. For example, if a set of nucleotide sequences is discovered thathave collective expression behavior that reliably distinguishes patientswith no histological rejection or graft dysfunction from all others, adiagnostic is developed that is used to screen patients for the need forbiopsy. Patients identified as having no rejection do not need biopsy,while others are subjected to a biopsy to further define the extent ofdisease. In another example, a diagnostic nucleotide set that determinescontinuing graft rejection associated with myocyte necrosis (>grade I)is used to determine that a patient is not receiving adequate treatmentunder the current treatment regimen. After increased or alteredimmunosuppressive therapy, diagnostic profiling is conducted todetermine whether continuing graft rejection is progressing. In yetanother example, a diagnostic nucleotide set(s) that determine apatient's rejection status and diagnose cytomegalovirus infection isused to balance immunosuppressive and anti-viral therapy.

The methods of this example are also applicable to cardiac xenograftmonitoring.

Example 6 Identification of Diagnostic Nucleotide Sets for Kidney andLiver Allograft Rejection

Diagnostic tests for rejection are identified using patient leukocyteexpression profiles to identify a molecular signature correlated withrejection of a transplanted kidney or liver. Blood, or other leukocytesource, samples are obtained from patients undergoing kidney or liverbiopsy following liver or kidney transplantation, respectively. Suchresults reveal the histological grade, i.e., the state and severity ofallograft rejection. Expression profiles are obtained from the samplesas described above, and the expression profile is correlated with biopsyresults. In the case of kidney rejection, clinical data is collectedcorresponding to urine output, level of creatine clearance, and level ofserum creatine (and other markers of renal function). Clinical datacollected for monitoring liver transplant rejection includes,biochemical characterization of serum markers of liver damage andfunction such as SGOT, SGPT, Alkaline phosphatase, GGT, Bilirubin,Albumin and Prothrombin time.

Leukocyte nucleotide sequence expression profiles are collected andcorrelated with important clinical states and outcomes in renal orhepatic transplantation. Examples of useful clinical correlates aregiven here:

1. Rejection episode of at least moderate histologic grade, whichresults in treatment of the patient with additional corticosteroids,anti-T cell antibodies, or total lymphoid irradiation.

2. The absence of histologic rejection and normal or unchanged allograftfunction (based on tests of renal or liver function listed above).

3. The presence of severe allograft dysfunction or worsening allograftdysfunction during the study period (based on tests of renal and hepaticfunction listed above).

4. Documented CMV infection by culture, histology, or PCR, and at leastone clinical sign or symptom of infection.

5. Specific graft biopsy rejection grades

6. Rejection of mild to moderate histologic severity promptingaugmentation of the patient's chronic immunosuppressive regimen

7. Infections other than CMV, esp. Epstein Barr virus (EBV)

8. Lymphoproliferative disorder (also called, post-transplant lymphoma)

9. Graft Failure or Retransplantation

10. Need for hemodialysis or other renal replacement therapy for renaltransplant patients.

11. Hepatic encephalopathy for liver transplant recipients.

12. All cause mortality

Subsets of the candidate library (or of a previously identifieddiagnostic nucleotide set), are identified, according to the aboveprocedures, that have predictive and/or diagnostic value for kidney orliver allograft rejection.

Example 7 Identification of a Diagnostic Nucleotide Set for Diagnosis ofCytomegalovirus

Cytomegalovirus is a very important cause of disease inimmunocompromised patients, for example, transplant patients, cancerpatients, and AIDS patients. The virus can cause inflammation anddisease in almost any tissue (particularly the colon, lung, bone marrowand retina). It is increasingly important to identify patients withcurrent or impending clinical CMV disease, particularly whenimmunosuppressive drugs are to be used in a patient, e.g. for preventingtransplant rejection. Leukocytes are profiled in patients with activeCMV, impending CMV, or no CMV. Expression profiles correlating withdiagnosis of active or impending CMV are identified. Subsets of thecandidate library (or a previously identified diagnostic nucleotide set)are identified, according to the above procedures that have predictivevalue for the diagnosis of active or impending CMV. Diagnosticnucleotide set(s) identified with predictive value for the diagnosis ofactive or impending CMV may be combined, or used in conjunction with,cardiac, liver and/or kidney allograft-related diagnostic gene set(s)(described in Examples 6 and 10).

In addition, or alternatively, CMV nucleotide sequences are obtained,and a diagnostic nucleotide set is designed using CMV nucleotidesequence. The entire sequence of the organism is known and all CMVnucleotide sequences can be isolated and added to the library using thesequence information and the approach described below. Known expressedgenes are preferred. Alternatively, nucleotide sequences are selected torepresent groups of CMV genes that are coordinately expressed (immediateearly genes, early genes, and late genes) (Spector et al. 1990,Stamminger et al. 1990).

Oligonucleotides were designed for CMV genes using the oligo designprocedures of Example 8. Probes were designed using the 14 genesequences shown here and were included on the array described in example9:

Cytomega- HCMVTRL2 (IRL2) 1893 . . . 2240 lovirus HCMVTRL7 (IRL7)complement(6595 . . . 6843) (CMV) HCMVUL21 complement(26497 . . . 27024)Accession HCMVUL27 complement(32831 . . . 34657) #X17403 HCMVUL33 43251. . . 44423 HCMVUL54 complement(76903 . . . 80631) HCMVUL75complement(107901 . . . 110132) HCMVUL83 complement(119352 . . . 121037)HCMVUL106 complement(154947 . . . 155324) HCMVUL109 complement(157514 .. . 157810) HCMVUL113 161503 . . . 162800 HCMVUL122 complement(169364 .. . 170599) HCMVUL123 complement(171006 . . . 172225) (last exon at3′-end) HCMVUS28 219200 . . . 220171

Diagnostic nucleotide set(s) for expression of CMV genes is used incombination with diagnostic leukocyte nucleotide sets for diagnosis ofother conditions, e.g. organ allograft rejection.

Using the techniques described in example 2 mononuclear samples from 180cardiac transplant recipients (enrolled in the study described inExample 5) were used for expression profiling with the leukocyte arrays.Of these samples 15 were associated with patients who had a diagnosis ofprimary or reactivation CMV made by culture, PCR or any specificdiagnostic test.

After preparation of RNA, amplification, labeling, hybridization,scanning, feature extraction and data processing were done as describedin Example 11 using the oligonucleotide microarrays described in Example9.

The resulting log ratio of expression of Cy3 (patient sample)/Cy5 (R50reference RNA) was used for analysis. Significance analysis formicroarrays (SAM, Tusher 2001, see Example 15) was applied to determinewhich genes were most significantly differentially expressed betweenthese 15 CMV patients and the 165 non-CMV patients (Table 12). 12 geneswere identified with a 0% FDR and 6 with a 0.1% FDR and are listed inTable 2. Some genes are represented by more than one oligonucleotide onthe array and for 2 genes, multiple oligonucleotides from the same geneare called significant (SEQ IDs: 3061, 3064: eomesodermin and 3031,3040, 104, 2736: small inducible cytokine A4).

Clinical variables were also included in the significance analysis. Forexample, the white blood cell count and the number of weeks posttransplant (for the patient at the time the sample was obtained) wereavailable for most of the 180 samples. The log of these variables wastaken and the variables were then used in the significance analysisdescribed above with the gene expression data. Both the white blood cellcount (0.1% FDR) and the weeks post transplant (0% FDR) appeared tocorrelate with CMV status. CMV patients were more likely to have samplesassociated with later post transplant data and the lower white bloodcell counts.

These genes and variables can be used alone or in association with othergenes or variables or with other genes to build a diagnostic gene set ora classification algorithm using the approaches described herein.

Primers for real-time PCR validation were designed for some of thesegenes as described in Example 13 and listed in Table 2C and the sequencelisting. Using the methods described in example 13, primers for GranzymeB were designed and used to validate expression findings from thearrays. 6 samples were tested (3 from patients with CMV and 3 frompatients without CMV). The gene was found to be differentially expressedbetween the patients with and without CMV (see example 13 for fulldescription). This same approach can be used to validate otherdiagnostic genes by real-time PCR. Diagnostic nucleotide sets can alsobe identified for a variety of other viral diseases (Table 1) using thissame approach.

cDNA microarrays may be used to monitor viral expression. In addition,these methods may be used to monitor other viruses, such as Epstein-Barrvirus, Herpes Simplex 1 and vesicular stomatitis virus.

Example 8 Design of Oligonucleotide Probes

By way of example, this section describes the design of fouroligonucleotide probes using Array Designer Ver 1.1 (Premier BiosoftInternational, Palo Alto, Calif.). The major steps in the process aregiven first.

Obtain best possible sequence of mRNA from GenBank. If a full-lengthsequence reference sequence is not available, a partial sequence isused, with preference for the 3′ end over the 5′ end. When the sequenceis known to represent the antisense strand, the reverse complement ofthe sequence is used for probe design. For sequences represented in thesubtracted leukocyte expression library that have no significant matchin GenBank at the time of probe design, our sequence is used.

Mask low complexity regions and repetitive elements in the sequenceusing an algorithm such as RepeatMasker.

Use probe design software, such as Array Designer, version 1.1, toselect a sequence of 50 residues with specified physical and chemicalproperties. The 50 residues nearest the 3′ end constitute a searchframe. The residues it contains are tested for suitability. If theydon't meet the specified criteria, the search frame is moved one residuecloser to the 5′ end, and the 50 residues it now contains are tested.The process is repeated until a suitable 50-mer is found.

If no such 50-mer occurs in the sequence, the physical and chemicalcriteria are adjusted until a suitable 50-mer is found.

Compare the probe to dbEST, the UniGene cluster set, and the assembledhuman genome using the BLASTn search tool at NCBI to obtain thepertinent identifying information and to verify that the probe does nothave significant similarity to more than one known gene.

Clone 40H12

Clone 40H12 was sequenced and compared to the nr, dbEST, and UniGenedatabases at NCBI using the BLAST search tool. The sequence matchedaccession number NM 002310, a ‘curated RefSeq project’ sequence, seePruitt et al. (2000) Trends Genet. 16:44-47, encoding leukemiainhibitory factor receptor (LIFR) mRNA with a reported E value of zero.An E value of zero indicates there is, for all practical purposes, nochance that the similarity was random based on the length of thesequence and the composition and size of the database. This sequence,cataloged by accession number NM_(—)002310, is much longer than thesequence of clone 40H12 and has a poly-A tail. This indicated that thesequence cataloged by accession number NM_(—)002310 is the sense strandand a more complete representation of the mRNA than the sequence ofclone 40H12, especially at the 3′ end. Accession number “NM_(—)002310”was included in a text file of accession numbers representing sensestrand mRNAs, and sequences for the sense strand mRNAs were obtained byuploading a text file containing desired accession numbers as an Entrezsearch query using the Batch Entrez web interface and saving the resultslocally as a FASTA file. The following sequence was obtained, and theregion of alignment of clone 40H12 is outlined:

CTCTCTCCCAGAACGTGTCTCTGCTGCAAGGCACCGGGCCCTTTCGCTCTGCAGAACTGCACTTGCAAGACCATTATCAACTCCTAATCCCAGCTCAGAAAGGGAGCCTCTGCGACTCATTCATCGCCCTCCAGGACTGACTGCATTGCACAGATGATGGATATTTACGTATGTTTGAAACGACCATCCTGGATGGTGGACAATAAAAGAATGAGGACTGCTTCAAATTTCCAGTGGCTGTTATCAACATTTATTCTTCTATATCTAATGAATCAAGTAAATAGCCAGAAAAAGGGGGCTCCTCATGATTTGAAGTGTGTAACTAACAATTTGCAAGTGTGGAACTGTTCTTGGAAAGCACCCTCTGGAACAGGCCGTGGTACTGATTATGAAGTTTGCATTGAAAACAGGTCCCGTTCTTGTTATCAGTTGGAGAAAACCAGTATTAAAATTCCAGCTCTTTCACATGGTGATTATGAAATAACAATAAATTCTCTACATGATTTTGGAAGTTCTACAAGTAAATTCACACTAAATGAACAAAACGTTTCCTTAATTCCAGATACTCCAGAGATCTTGAATTTGTCTGCTGATTTCTCAACCTCTACATTATACCTAAAGTGGAACGACAGGGGTTCAGTTTTTCCACACCGCTCAAATGTTATCTGGGAAATTAAAGTTCTACGTAAAGAGAGTATGGAGCTCGTAAAATTAGTGACCCACAACACAACTCTGAATGGCAAAGATACACTTCATCACTGGAGTTGGGCCTCAGATATGCCCTTGGAATGTGCCATTCATTTTGTGGAAATTAGATGCTACATTGACAATCTTCATTTTTCTGGTCTCGAAGAGTGGAGTGACTGGAGCCCTGTGAAGAACATTTCTTGGATACCTGATTCTCAGACTAAGGTTTTTCCTCAAGATAAAGTGATACTTGTAGGCTCAGACATAACATTTTGTTGTGTGAGTCAAGAAAAAGTGTTATCAGCACTGATTGGCCATACAAACTGCCCCTTGATCCATCTTGATGGGGAAAATGTTGCAATCAAGATTCGTAATATTTCTGTTTCTGCAAGTAGTGGAACAAATGTAGTTTTTACAACCGAAGATAACATATTTGGAACCGTTATTTTTGCTGGATATCCACCAGATACTCCTCAACAACTGAATTGTGAGACACATGATTTAAAAGAAATTATATGTAGTTGGAATCCAGGAAGGGTGACAGCGTTGGTGGGCCCACGTGCTACAAGCTACACTTTAGTTGAAAGTTTTTCAGGAAAATATGTTAGACTTAAAAGAGCTGAAGCACCTACAAACGAAAGCTATCAATTATTATTTCAAATGCTTCCAAATCAAGAAATATATAATTTTACTTTGAATGCTCACAATCCGCTGGGTCGATCACAATCAACAATTTTAGTTAATATAACTGAAAAAGTTTATCCCCATACTCCTACTTCATTCAAAGTGAAGGATATTAATTCAACAGCTGTTAAACTTTCTTGGCATTTACCAGGCAACTTTGCAAAGATTAATTTTTTATGTGAAATTGAAATTAAGAAATCTAATTCAGTACAAGAGCAGCGGAATGTCACAATCAAAGGAGTAGAAAATTCAAGTTATCTTGTTGCTCTGGACAAGTTAAATCCATACACTCTATATACTTTTCGGATTCGTTGTTCTACTGAAACTTTCTGGAAATGGAGCAAATGGAGCAATAAAAAACAACATTTAACAACAGAAGCCAGTCCTTCAAAGGGGCCTGATACTTGGAGAGAGTGGAGTTCTGATGGAAAAAATTTAATAATCTATTGGAAGCCTTTACCCATTAATGAAGCTAATGGAAAAATACTTTCCTACAATGTATCGTGTTCATCAGATGAGGAAACACAGTCCCTTTCTGAAATCCCTGATCCTCAGCACAAAGCAGAGATACGACTTGATAAGAATGACTACATCATCAGCGTAGTGGCTAAAAATTCTGTGGGCTCATCACCACCTTCCAAAATAGCGAGTATGGAAATTCCAAATGATGATCTCAAAATAGAACAAGTTGTTGGGATGGGAAAGGGGATTCTCCTCACCTGGCATTACGACCCCAACATGACTTGCGACTACGTCATTAAGTGGTGTAACTCGTCTCGGTCGGAACCATGCCTTATGGACTGGAGAAAAGTTCCCTCAAACAGCACTGAAACTGTAATAGAATCTGATGAGTTTCGACCAGGTATAAGATATAATTTTTTCCTGTATGGATGCAGAAATCAAGGATATCAATTATTACGCTCCATGATTGGATATATAGAAGAATTGGCTCCCATTGTTGCACCAAATTTTACTGTTGAGGATACTTCTGCAGATTCGATATTAGTAAAATGGGAAGACATTCCTGTGGAAGAACTTAGAGGCTTTTTAAGAGGATATTTGTTTTACTTTGGAAAAGGAGAAAGAGACACATCTAAGATGAGGGTTTTAGAATCAGGTCGTTCTGACATAAAAGTTAAGAATATTACTGACATATCCCAGAAGACACTGAGAATTGCTGATCTTCAAGGTAAAACAAGTTACCACCTGGTCTTGCGAGCCTATACAGATGGTGGAGTGGGCCCGGAGAAGAGTATGTATGTGGTGACAAAGGAAAATTCTGTGGGATTAATTATTGCCATTCTCATCCCAGTGGCAGTGGCTGTCATTGTTGGAGTGGTGACAAGTATCCTTTGCTATCGGAAACGAGAATGGATTAAAGAAACCTTCTACCCTGATATTCCAAATCCAGAAAACTGTAAAGCATTACAGTTTCAAAAGAGTGTCTGTGAGGGAAGCAGTGCTCTTAAAACATTGGAAATGAATCCTTGTACCCCAAATAATGTTGAGGTTCTGGAAACTCGATCAGCATTTCCTAAAATAGAAGATACAGAAATAATTTCCCCAGTAGCTGAGCGTCCTGAAGATCGCTCTGATGCAGAGCCTGAAAACCATGTGGTTGTGTCCTATTGTCCACCCATCATTGAGGAAGAAATACCAAACCCAGCCGCAGATGAAGCTGGAGGGACTGCACAGGTTATTTACATTGATGTTCAGTCGATGTATCAGCCTCAAGCAAAACCAGAAGAAGAACAAGAAAATGACCCTGTAGGAGGGGCAGGCTATAAGCCACAGATGCACCTCCCCATTAATTCTACTGTGGAAGATATAGCTGCAGAAGAGGACTTAGATAAAACTGCGGGTTACAGACCTCAGGCCAATGTAAATACATGGAATTTAGTGTCTCCAGACTCTCCTAGATCCATAGACAGCAACAGTGAGATTGTCTCATTTGGAAGTCCATGCTCCATTAATTCCCGACAATTTTTGATTCCTCCTAAAGATGAAGACTCTCCTAAATCTAATGGAGGAGGGTGGTCCTTTACAAACTTTTTTCAGAACAAACCAAACGATTAACAGTGTCACCGTGTCACTTCAGTCAGCCATCTCAATAAGCTCTTACTGCTAGTGTTGCTACATCAGCACTGGGCATTCTTGGAGGGATCCTGTGAAGTATTGTTAGGAGGTGAACTTCACTACATGTTAAGTTACACTGAAAGTTCATGTGCTTTTAATGTAGTCTAAAAGCCAAAGTATAGTGACTCAGAATCCTCAATCCACAAAACTCAAGATTGGGAGCTCTTTGTGATCAAGCCAAAGAATTCTCATGTACTCTACCTTCAAGAAGCATTTCAAGGCTAATACCTACTTGTACGTACATGTAAAACAAATCCCGCCGCAACTGTTTTCTGTTCTGTTGTTTGTGGTTTTCTCATATGTATACTTGGTGGAATTGTAAGTGGATTTGCAGGCCAGGGAGAAAATGTCCAAGTAACAGGTGAAGTTTATTTGCCTGACGTTTACTCCTTTCTAGATGAAAACCAAGCACAGATTTTAAAACTTCTAAGATTATTCTCCTCTATCCACAGCATTCACAAAAATTAATATAATTTTTAATGTAGTGACAGCGATTTAGTGTTTTGTTTGATAAAGTATGCTTATTTCTGTGCCTACTGTATAATGGTTATCA

CTTTTCATATTTAAGGCAAAAGTACTTGAAAATTTTAATTGTCCGAATAAGATATGTCTTTTTTGTTTGTTTTTTTTGGTTGGTTGTTTGTTTTTTATCATCTGAGATTCTGTAATGTATTTGCAAATAATGGATCAATTAATTTTTTTTGAAGCTCATATTGTATCTTTTTAAAAACCATGTTGTGGAAAAAAAGCCAGAGTGACAATGACAAAATCTATTTAGGAACTCTGTGTATGAATCCTGATTTTAACTGCTAGGATTCAGCTAAATTTCTGAGCTTTATGATCTGTGGAAATTTGGAATGAAATCGAATTCATTTTGTACATACATAGTATATTAAAACTATATAATAGTTCATAGAAATGTTCAGTAATGAAAAAATATATCCAATCAGAGCCATCCCGAAAAAAAAAAAAAAA (SEQ ID NO: 3101)

The FASTA file, including the sequence of NM_(—)002310, was masked usingthe RepeatMasker web interface (Smit, A F A & Green, P RepeatMasker athttp://ftp.genome.washington.edu/RM/RepeatMasker.html, Smit and Green).Specifically, during masking, the following types of sequences werereplaced with “N's”: SINE/MIR & LINE/L2, LINE/L1, LTR/MaLR,LTR/Retroviral, Alu, and other low informational content sequences suchas simple repeats. Below is the sequence following masking:

CTCTCTCCCAGAACGTGTCTCTGCTGCAAGGCACCGGGCCCTTTCGCTCTGCAGAACTGCACTTGCAAGACCATTATCAACTCCTAATCCCAGCTCAGAAAGGGAGCCTCTGCGACTCATTCATCGCCCTCCAGGACTGACTGCATTGCACAGATGATGGATATTTACGTATGTTTGAAACGACCATCCTGGATGGTGGACAATAAAAGAATGAGGACTGCTTCAAATTTCCAGTGGCTGTTATCAACATTTATTCTTCTATATCTAATGAATCAAGTAAATAGCCAGAAAAAGGGGGCTCCTCATGATTTGAAGTGTGTAACTAACAATTTGCAAGTGTGGAACTGTTCTTGGAAAGCACCCTCTGGAACAGGCCGTGGTACTGATTATGAAGTTTGCATTGAAAACAGGTCCCGTTCTTGTTATCAGTTGGAGAAAACCAGTATTAAAATTCCAGCTCTTTCACATGGTGATTATGAAATAACAATAAATTCTCTACATGATTTTGGAAGTTCTACAAGTAAATTCACACTAAATGAACAAAACGTTTCCTTAATTCCAGATACTCCAGAGATCTTGAATTTGTCTGCTGATTTCTCAACCTCTACATTATACCTAAAGTGGAACGACAGGGGTTCAGTTTTTCCACACCGCTCAAATGTTATCTGGGAAATTAAAGTTCTACGTAAAGAGAGTATGGAGCTCGTAAAATTAGTGACCCACAACACAACTCTGAATGCCAAAGATACACTTCATCACTGGAGTTGGGCCTCAGATATGCCCTTGGAATGTGCCATTCATTTTGTGGAAATTAGATGCTACATTGACAATCTTCATTTTTCTGGTCTCGAAGAGTGGAGTGACTGGAGCCCTGTGAAGAACATTTCTTGGATACCTGATTCTCAGACTAAGGTTTTTCCTCAAGATAAAGTGATACTTGTAGGCTCAGACATAACATTTTGTTGTGTGAGTCAAGAAAAAGTGTTATCAGCACTGATTGGCCATACAAACTGCCCCTTGATCCATCTTGATGGGGAAAATGTTGCAATCAAGATTCGTAATATTTCTGTTTCTGCAAGTAGTGGAACAAATGTAGTTTTTACAACCGAAGATAACATATTTGGAACCGTTATTTTTGCTGGATATCCACCAGATACTCCTCAACAACTGAATTGTGAGACACATGATTTAAAAGAAATTATATGTAGTTGGAATCCAGGAAGGGTGACAGCGTTGGTGGGCCCACGTGCTACAAGCTACACTTTAGTTGAAAGTTTTTCAGGAAAATATGTTAGACTTAAAAGAGCTGAAGCACCTACAAACGAAAGCTATCAATTATTATTTCAAATGCTTCCAAATCAAGAAATATATAATTTTACTTTGAATGCTCACAATCCGCTGGGTCGATCACAATCAACAATTTTAGTTAATATAACTGAAAAAGTTTATCCCCATACTCCTACTTCATTCAAAGTGAAGGATATTAATTCAACAGCTGTTAAACTTTCTTGGCATTTACCAGGCAACTTTGCAAAGATTAATTTTTTATGTGAAATTGAAATTAAGAAATCTAATTCAGTACAAGAGCAGCGGAATGTCACAATCAAAGGAGTAGAAAATTCAAGTTATCTTGTTGCTCTGGACAAGTTAAATCCATACACTCTATATACTTTTCGGATTCGTTGTTCTACTGAAACTTTCTGGAAATGGAGCAAATGGAGCAATAAAAAACAACATTTAACAACAGAAGCCAGTCCTTCAAAGGGGCCTGATACTTGGAGAGAGTGGAGTTCTGATGGAAAAAATTTAATAATCTATTGGAAGCCTTTACCCATTAATGAAGCTAATGGAAAAATACTTTCCTACAATGTATCGTGTTCATCAGATGAGGAAACACAGTCCCTTTCTGAAATCCCTGATCCTCAGCACAAAGCAGAGATACGACTTGATAAGAATGACTACATCATCAGCGTAGTGGCTAAAAATTCTGTGGGCTCATCACCACCTTCCAAAATAGCGAGTATGGAAATTCCAAATGATGATCTCAAAATAGAACAAGTTGTTGGGATGGGAAAGGGGATTCTCCTCACCTGGCATTACGACCCCAACATGACTTGCGACTACGTCATTAAGTGGTGTAACTCGTCTCGGTCGGAACCATGCCTTATGGACTGGAGAAAAGTTCCCTCAAACAGCACTGAAACTGTAATAGAATCTGATGAGTTTCGACCAGGTATAAGATATAATTTTTTCCTGTATGGATGCAGAAATCAAGGATATCAATTATTACGCTCCATGATTGGATATATAGAAGAATTGGCTCCCATTGTTGCACCAAATTTTACTGTTGAGGATACTTCTGCAGATTCGATATTAGTAAAATGGGAAGACATTCCTGTGGAAGAACTTAGAGGCTTTTTAAGAGGATATTTGTTTTACTTTGGAAAAGGAGAAAGAGACACATCTAAGATGAGGGTTTTAGAATCAGGTCGTTCTGACATAAAAGTTAAGAATATTACTGACATATCCCAGAAGACACTGAGAATTGCTGATCTTCAAGGTAAAACAAGTTACCACCTGGTCTTGCGAGCCTATACAGATGGTGGAGTGGGCCCGGAGAAGAGTATGTATGTGGTGACAAAGGAAAATTCTGTGGGATTAATTATTGCCATTCTCATCCCAGTGGCAGTGGCTGTCATTGTTGGAGTGGTGACAAGTATCCTTTGCTATCGGAAACGAGAATGGATTAAAGAAACCTTCTACCCTGATATTCCAAATCCAGAAAACTGTAAAGCATTACAGTTTCAAAAGAGTGTCTGTGAGGGAAGCAGTGCTCTTAAAACATTGGAAATGAATCCTTGTACCCCAAATAATGTTGAGGTTCTGGAAACTCGATCAGCATTTCCTAAAATAGAAGATACAGAAATAATTTCCCCAGTAGCTGAGCGTCCTGAAGATCGCTCTGATGCAGAGCCTGAAAACCATGTGGTTGTGTCCTATTGTCCACCCATCATTGAGGAAGAAATACCAAACCCAGCCGCAGATGAAGCTGGAGGGACTGCACAGGTTATTTACATTGATGTTCAGTCGATGTATCAGCCTCAAGCAAAACCAGAAGAAGAACAAGAAAATGACCCTGTAGGAGGGGCAGGCTATAAGCCACAGATGCACCTCCCCATTAATTCTACTGTGGAAGATATAGCTGCAGAAGAGGACTTAGATAAAACTGCGGGTTACAGACCTCAGGCCAATGTAAATACATGGAATTTAGTGTCTCCAGACTCTCCTAGATCCATAGACAGCAACAGTGAGATTGTCTCATTTGGAAGTCCATGCTCCATTAATTCCCGACAATTTTTGATTCCTCCTAAAGATGAAGACTCTCCTAAATCTAATGGAGGAGGGTGGTCCTTTACAAACTTTTTTCAGAACAAACCAAACGATTAACAGTGTCACCGTGTCACTTCAGTCAGCCATCTCAATAAGCTCTTACTGCTAGTGTTGCTACATCAGCACTGGGCATTCTTGGAGGGATCCTGTGAAGTATTGTTAGGAGGTGAACTTCACTACATGTTAAGTTACACTGAAAGTTCATGTGCTTTTAATGTAGTCTAAAAGCCAAAGTATAGTGACTCAGAATCCTCAATCCACAAAACTCAAGATTGGGAGCTCTTTGTGATCAAGCCAAAGAATTCTCATGTACTCTACCTTCAAGAAGCATTTCAAGGCTAATACCTACTTGTACGTACATGTAAAACAAATCCCGCCGCAACTGTTTTCTGTTCTGTTGTTTGTGGTTTTCTCATATGTATACTTGGTGGAATTGTAAGTGGATTTGCAGGCCAGGGAGAAAATGTCCAAGTAACAGGTGAAGTTTATTTGCCTGACGTTTACTCCTTTCTAGATGAAAACCAAGCACAGATTTTAAAACTTCTAAGATTATTCTCCTCTATCCACAGCATTCACNNNNNNNNNNNNNNNNNNNNNNGTAGTGACAGCGATTTAGTGTTTTGTTTGATAAAGTATGCTTATTTCTGTGCCTACTGTATAATGGTTATCAAACAGTTGTCT

CTTTTCATATTTAAGGCAAAAGTACTTGAAAATTTTAAGTGTCCGAATAAGATATGTCTTTTTTGTTTGTTTTTTTTGGTTGGTTGTTTGTTTTTTATCATCTGAGATTCTGTAATGTATTTGCAAATAATGGATCAATTAATTTTTTTTGAAGCTCATATTGTATCTTTTTAAAAACCATGTTGTGGAAAAAAGCCAGAGTGACAAGTGACAAAATCTATTTAGGAACTCTGTGTATGAATCCTGATTTTAACTGCTAGGATTCAGCTAAATTTCTGAGCTTTATGATCTGTGGAAATTTGGAATGAAATCGAATTCATTTTGTACATACATAGTATATTAAAACTATATAATGATTCATAGAAATGTTCAGTAATGAAAAAATATATCCAATCAGAGCCATCCCGAAAAAAAAAAAAAAA (SEQ ID NO: 3102).

The length of this sequence was determined using batch, automatedcomputational methods and the sequence, as sense strand, its length, andthe desired location of the probe sequence near the 3′ end of the mRNAwas submitted to Array Designer Ver 1.1 (Premier Biosoft International,Palo Alto, Calif.). Search quality was set at 100%, number of bestprobes set at 1, length range set at 50 base pairs, Target Tm set at 75C. degrees plus or minus 5 degrees, Hairpin max deltaG at 6.0-kcal/mol.,Self dimmer max deltaG at 6.0-kcal/mol, Run/repeat (dinucleotide) maxlength set at 5, and Probe site minimum overlap set at 1. When none ofthe 49 possible probes met the criteria, the probe site would be moved50 base pairs closer to the 5′ end of the sequence and resubmitted toArray Designer for analysis. When no possible probes met the criteria,the variation on melting temperature was raised to plus and minus 8degrees and the number of identical basepairs in a run increased to 6 sothat a probe sequence was produced.

In the sequence above, using the criteria noted above, Array DesignerVer 1.1 designed a probe corresponding to oligonucleotide number 3037and is indicated by underlining in the sequence above. It has a meltingtemperature of 68.4 degrees Celsius and a max run of 6 nucleotides andrepresents one of the cases where the criteria for probe design in ArrayDesigner Ver 1.1 were relaxed in order to obtain an oligonucleotide nearthe 3′ end of the mRNA (Low melting temperature was allowed).

Clone 463D12

Clone 463D12 was sequenced and compared to the nr, dbEST, and UniGenedatabases at NCBI using the BLAST search tool. The sequence matchedaccession number AI184553, an EST sequence with the definition line“qd60a05.x1 Soares_testis_NHT Homo sapiens cDNA clone IMAGE:1733840 3′similar to gb:M29550 PROTEIN PHOSPHATASE 2B CATALYTIC SUBUNIT 1 (HUMAN),mRNA sequence.” The E value of the alignment was 1.00×10⁻¹¹⁸. TheGenBank sequence begins with a poly-T region, suggesting that it is theantisense strand, read 5′ to 3′. The beginning of this sequence iscomplementary to the 3′ end of the mRNA sense strand. The accessionnumber for this sequence was included in a text file of accessionnumbers representing antisense sequences. Sequences for antisense strandmRNAs were obtained by uploading a text file containing desiredaccession numbers as an Entrez search query using the Batch Entrez webinterface and saving the results locally as a FASTA file. The followingsequence was obtained, and the region of alignment of clone 463D12 isoutlined:

TTTTTTTTTTTTTTCTTAAATAGCATTTATTTTCTCTCAAAAAGCCTATTATGTACTAACAAGTGTTCCTCTAAATTAGAAAGGCATCACTACTAAAATTTTATACATATTTTTTATATAAGAGAAGGAATATTGGGTTACAATCTGAATTTCTCTTTATGATTTCTCTTAAAGTATAGAACAGCTATTAAAATGACTAATATTGCTAAAATGAAGGCTACTAAATTTCCCCAAGAATTTCGGTGGAATGCCCAAAAATGGTGTTAAGATATGCAGAAGGGCCCATTTCAAGCAAAGCAATCTCTCCACCCCTTCATAAAAGATTTAAGCTAAAAAAAAAAAAAA

CGGTAAAGACCACGTGAAGACATCCATAAAATTAGGCAACCAGTAAAGATGTGGAGAACCAGTAAACTGTCGAAATTCATCACATTATTTTCATACTTTAATACAGCAGCTTTAATTATTGGAGAACATCAAAGTAATTAGGTGCCGAAAAACATTGTTATTAATGAAGGGAACCCCTGACGTTTGACCTTTTCTGTACCATCTATAGCCCTGGACTTGA (SEQ ID NO: 3103)

The FASTA file, including the sequence of AA184553, was then maskedusing the RepeatMasker web interface, as shown below. The region ofalignment of clone 463D12 is outlined.

TTTTTTTTTTTTTTCTTAAATAGCATTTATTTTCTCTCAAAAAGCCTATTATGTACTAACAAGTGTTCCTCTAAATTAGAAAGGCATCACTACNNNNNNNNNNNNNNNNNNNNNNNNNNNNGAGAAGGAATATTGGGT

AAGGGCCCATTTCAAGCAAAGCAATCTCTCCACCCCTTCATAAAAGATTTAAGCTAAAAAAAAAAAAAA

CGGTAAAGACCACGTGAAGACATCCATAAAATTAGGCAACCAGTAAAGATGTGGAGAACCAGTAAACTGTCGAAATTCATCACATTATTTTCATACTTTAATACAGCAGCTTTAATTATTGGAGAACATCAAAGTAATTAGGTGCCGAAAAACATTGTTATTAATGAAGGGAACCCCTGACGTTTGACCTTTTCTGTACCATCTATAGCCCTGGACTTGA Masked version of 463D12 sequence. (SEQ ID NO: 3104)

The sequence was submitted to Array Designer as described above,however, the desired location of the probe was indicated at base pair 50and if no probe met the criteria, moved in the 3′ direction. Thecomplementary sequence from Array Designer was used, because theoriginal sequence was antisense. The oligonucleotide designed by ArrayDesigner corresponds to oligonucleotide number 3054 and is complementaryto the underlined sequence above. The probe has a melting temperature of72.7 degrees centigrade and a max run of 4 nucleotides.

Clone 72D4

Clone 72D4 was sequenced and compared to the nr, dbEST, and UniGenedatabases at NCBI using the BLAST search tool. No significant matcheswere found in any of these databases. When compared to the human genomedraft, significant alignments were found to three consecutive regions ofthe reference sequence NT_(—)008060, as depicted below, suggesting thatthe insert contains three spliced exons of an unidentified gene.

Residue numbers on Matching residue clone 72D4 sequence numbers onNT_008060  1-198 478646-478843 197-489 479876-480168 491-585489271-489365

Because the reference sequence contains introns and may represent eitherthe coding or noncoding strand for this gene, BioCardia's own sequencefile was used to design the oligonucleotide. Two complementary probeswere designed to ensure that the sense strand was represented. Thesequence of the insert in clone 72D4 is shown below, with the threeputative exons outlined.

AAAAAC (SEQ ID NO: 3106)

The sequence was submitted to RepeatMasker, but no repetitive sequenceswere found. The sequence shown above was used to design the two 50-merprobes using Array Designer as described above. The probes are shown inbold typeface in the sequence depicted below. The probe in the sequenceis oligonucleotide number 3020 (SEQ ID NO: 3020) and the complementaryprobe is oligonucleotide number 318 (SEQ ID NO:318). A portion of thetarget sequence is listed below (SEQ ID: 3106).

CAGGTCACACAGCACATCAGTGGCTACATGTGAGCTCAGACCTGGGTCTGCTGCTGTCTGTCTTCCCAATATCCATGACCTTGACTGATGCAGGTGTCTAGGGATACGTCCATCCCCGTCCTGCTGGAGCCCAGAGCACGGAAGCCTGGCCCTCCGAGGAGACAGAAGGGAGTGTCGGACACCATGACGAGAGCTTGGCAGAATAAATAACTTCTTTAAACAATTTTACGGCATGAAGAAATCTGGACCAGTTTATTAAATGGGATTTCTGCCACAAACCTTGGAAGAATCACATCATCTTANNCCCAAGTGAAAACTGTGTTGCGTAACAAAGAACATGACTGCGCTCCACACATACATCATTGCCCGGCGAGGCGGGACACAAGTCAACGACGGAACACTTGAGACAGGCCTACAACTGTGCACGGGTCAGAAGCAAGTTTAAGCCATACTTGCTGCAGTGAGACTACATTTCTGTCTATAGAAGATACCTGACTTGATCTGTTTTTCAGCTCCAGTTCCCAGATGTGC                                     ←----GTCAAGGGTCTACACGGTGTTGTGGTCCCCAAGTATCACCTTCCAATTTCTGGGAG--→CACAACACCAGGGGTTCATAGTGGAAGGTTAAAG-5′CAGTGCTCTGGCCGGATCCTTGCCGCGCGGATAAAAACT---→Confirmation of Probe Sequence

Following probe design, each probe sequence was confirmed by comparingthe sequence against dbEST, the UniGene cluster set, and the assembledhuman genome using BLASTn at NCBI. Alignments, accession numbers, ginumbers, UniGene cluster numbers and names were examined and the mostcommon sequence used for the probe.

Example 9 Production of an Array of 8000 Spotted 50mer Oligonucleotides

We produced an array of 8000 spotted initial candidate 50meroligonucleotides. Example 8 exemplifies the design and selection ofprobes for this array.

Sigma-Genosys (The Woodlands, Tex.) synthesized unmodified 50-meroligonucleotides using standard phosphoramidite chemistry, with astarting scale of synthesis of 0.05 μmole (see, e.g., R. Meyers, ed.(1995) Molecular Biology and Biotechnology: A Comprehensive DeskReference). Briefly, to begin synthesis, a 3′ hydroxyl nucleoside with adimethoxytrityl (DMT) group at the 5′ end was attached to a solidsupport. The DMT group was removed with trichloroacetic acid (TCA) inorder to free the 5′-hydroxyl for the coupling reaction. Next, tetrazoleand a phosphoramidite derivative of the next nucleotide were added. Thetetrazole protonates the nitrogen of the phosphoramidite, making itsusceptible to nucleophilic attack. The DMT group at the 5′-end of thehydroxyl group blocks further addition of nucleotides in excess. Next,the inter-nucleotide linkage was converted to a phosphotriester bond inan oxidation step using an oxidizing agent and water as the oxygendonor. Excess nucleotides were filtered out and the cycle for the nextnucleotide was started by the removal of the DMT protecting group.Following the synthesis, the oligo was cleaved from the solid support.The oligonucleotides were desalted, resuspended in water at aconcentration of 100 or 200 μM, and placed in 96-deep well format. Theoligonucleotides were re-arrayed into Whatman Uniplate 384-wellpolyproylene V bottom plates. The oligonucleotides were diluted to afinal concentration 30 μM in 1× Micro Spotting Solution Plus(Telechem/arrayit.com, Sunnyvale, Calif.) in a total volume of 15 μl. Intotal, 8,031 oligonucleotides were arrayed into twenty-one 384-wellplates.

Arrays were produced on Telechem/arrayit.com Super amine glasssubstrates (Telechem/arrayit.com), which were manufactured in 0.1 mmfiltered clean room with exact dimensions of 25×76×0.96 mm. The arrayswere printed using the Virtek Chipwriter with a Telechem 48 pin MicroSpotting Printhead. The Printhead was loaded with 48 Stealth SMP3BTeleChem Micro Spotting Pins, which were used to print oligonucleotidesonto the slide with the spot size being 110-115 microns in diameter.

Example 10 Identification of Diagnostic Nucleotide Sets for Diagnosis ofCardiac Allograft Rejection

Genes were identified which have expression patterns useful for thediagnosis and monitoring of cardiac allograft rejection. Further, setsof genes that work together in a diagnostic algorithm for allograftrejection were identified. Patients, patient clinical data and patientsamples used in the discovery of markers below were derived from aclinical study described in example 5.

The collected clinical data is used to define patient or sample groupsfor correlation of expression data. Patient groups are identified forcomparison, for example, a patient group that possesses a useful orinteresting clinical distinction, verses a patient group that does notpossess the distinction. Measures of cardiac allograft rejection arederived from the clinical data described above to divide patients (andpatient samples) into groups with higher and lower rejection activityover some period of time or at any one point in time. Such data arerejection grade as determined from pathologist reading of the cardiacbiopsies and data measuring progression of end-organ damage, includingdepressed left ventricular dysfunction (decreased cardiac output,decreased ejection fraction, clinical signs of low cardiac output) andusage of inotropic agents (Kobashigawa 1998).

Expression profiles correlating with occurrence of allograft rejectionare identified, including expression profiles corresponding to end-organdamage and progression of end-organ damage.

Expression profiles are identified predicting allograft rejection, andresponse to treatment or likelihood of response to treatment. Subsets ofthe candidate library (or a previously identified diagnostic nucleotideset) are identified, that have predictive value for the presence ofallograft rejection or prediction of allograft rejection or end organdamage.

Mononuclear RNA samples were collected from patients who had recentlyundergone a cardiac allograft transplantation using the protocoldescribed in example 2. The allograft rejection status at the time ofsample collection was determined by examination of cardiac biopsies asdescribed in example 5.

180 samples were included in the analysis. Each patient sample wasassociated with a biopsy and clinical data collected at the time of thesample. The cardiac biopsies were graded by a pathologist at the localcenter and by a centralized pathologist who read the biopsy slides fromall four local centers in a blinded manner. Biopsy grades included 0,1A, 1B, 2, 3A, and 3B. No grade 4 rejection was identified. Dependentvariables were developed based on these grades using either the localcenter pathology reading or the higher of the two readings, local orcentralized. The dependent variables used for correlation of geneexpression profiles with cardiac allograft rejection are shown in Table4. Dependent variables are used to create classes of samplescorresponding to the presence or absence of rejection.

Clinical data were also used to determine criteria for including samplesin the analysis. The strictest inclusion criteria required that samplesbe from patients who did not have a bacterial or viral infection, wereat least two weeks post cardiac transplant and were not currentlyadmitted to the hospital. A second inclusion criteria (inclusion 2)reduced the post-transplant criteria to 1 week and eliminated thehospital admission criteria.

After preparation of RNA (example 2), amplification, labeling,hybridization, scanning, feature extraction and data processing weredone as described in Example 11, using the oligonucleotide microarraysdescribed in Example 9. The resulting log ratio of expression of Cy3(patient sample)/Cy5 (R50 reference RNA) was used for analysis. Thisdataset is called the “static” data. A second type of dataset,referenced, was derived from the first. These datasets compared the geneexpression log ratio in each sample to a baseline sample from the samepatient using the formula:ref log ratio=(log ratio_(sample))−(log ratio_(baseline))

Two referenced datasets were used, named “0 HG” and “Best 0”. Thebaseline for 0 HG was a Grade 0 sample from the same patient as thesample, using the highest grade between the centralized and localpathologists. The baseline for Best 0 was a Grade 0 sample from the samepatient as the sample, using both the local and centralized readerbiopsy grade data. When possible a Grade 0 prior to the sample was usedas the baseline in both referenced datasets.

The datasets were also divided into subsets to compare analysis betweentwo subsets of roughly half of the data. The types of subsetsconstructed were as follows. First half/second half subsets were thefirst half of the samples and the second half of the samples from adataset ordered by sample number. Odd/even subsets used the same source,a dataset ordered by sample number, but the odd subset consisted ofevery 2^(nd) sample starting with the first and the even subsetconsisted of every 2^(nd) sample starting with the second sample, Center14/other subsets were the same datasets, divided by transplant hospital.The center 14 subset consisted of all samples from patients at center14, while the other subset consisted of all samples from the other threecenters (12,13, and 15).

Initially, significance analysis for microarrays (SAM, Tusher 2001,Example 15) was used to discover genes that were differentiallyexpressed between the rejection and no-rejection groups. Ninety-sixdifferent combinations of dependent variables, inclusion criteria,static/referenced, and data subsets were used in SAM analysis to developthe primary lists of genes significantly differentially expressedbetween rejection and no-rejection. The most significant of these geneswere chosen based on the following criteria. Tier 1 genes were thosewhich appeared with an FDR of less than 20% in identical analyses in twoindependent subsets. Tier 2 genes were those which appeared in the top20 genes on the list with an FDR less than 20% more than 50% of the timeover all dependent variables with the inclusion criteria, andstatic/referenced constant. Tier 3 genes were those that appeared morethan 50% of the time with an FDR less than 20% more than 50% of the timeover all dependent variables with the inclusion criteria, andstatic/referenced constant. The genes that were identified by theanalysis as statistically differentially expressed between rejection andno rejection are shown in Table 2.

SAM chooses genes as significantly different based on the magnitude ofthe difference between the groups and the variation among the sampleswithin each group. An example of the difference between some Grade 0 andsome Grade 3A samples for 9 genes is shown in FIG. 7A.

Additionally, many of these same combinations were used in theSupervised Harvesting of Expression Trees (SHET, Hastie et al. 2001)algorithm (see example 15) to identify markers that the algorithm choseas the best to distinguish between the rejection and no rejectionclasses using a bias factor of 0.01. The top 20 or 30 terms were takenfrom the SHET output and among all comparisons in either the static orreferenced data the results were grouped. Any gene found in the top 5terms in more than 50% of the analyses was selected to be in group B1(Table 2). The occurrences of each gene were tabulated over all SHETanalysis (for either static or referenced data) and the 10 genes thatoccurred the most were selected to be in group B2 (Table 2).

An additional classification method used was CART (Salford Systems, SanDiego, example 15). Either the static or referenced dataset was reducedto only the genes for which expression values (log ratios) were presentin at least 80% of the samples. These data were used in CART with thedefault settings, using the Symmetric Gini algorithm. Each of thedependent variables was used with both the full sample set and thestrict inclusion criteria. Two groups of genes were identified. Group C1were those genes that were a primary splitter (1^(st) decision node).Group C2 genes were the 10 genes that occurred as splitters the mostoften over all these analyses.

Two other classification models were developed and their best genesidentified as markers of cardiac allograft rejection. Group D genes wereidentified from a set of 59 samples, referenced data, local biopsyreading grade, using logistic regression. Group E genes were identifiedfrom the primary static dataset using a K-nearest neighborclassification algorithm.

Both hierarchical clustering (Eisen et al. 1998) and CART were used toidentify surrogates for each identified marker. Hierarchical clusteringsurrogates are genes co-expressed in these and were chosen from thenearest branches of the dendrogram. CART surrogates were identified byCART as the surrogates for those genes chosen as primary splitters atdecision nodes.

Primers for real-time PCR validation were designed for each of themarker genes as described in Example 13.

CART was used to build a decision tree for classification of samples asrejection or no-rejection using the gene expression data from thearrays. The analysis identified sets of genes that can be used togetherto accurately identify samples derived from cardiac allograft transplantpatients. The set of genes and the identified threshold expressionlevels for the decision tree are referred to as a “models”. This modelcan be used to predict the rejection state of an unknown sample. Theinput data were the static expression data (log ratio) and thereferenced expression data (log ratio referenced to the best availablegrade 0 from either the centralized reader or the local reader) for 139of our top marker genes. These two types of expression data were enteredinto the CART software as independent variables. The dependent variablewas rejection state, defined for this model as no rejection=grade 0 andrejection=grade 3A. Samples were eliminated from consideration in thetraining set if they were from patients with either bacterial or viralinfection or were from patients who were less than two weekspost-transplant. The method used was Symmetric Gini, allowing linearcombinations of independent variables. The costs were set to 1 for bothfalse negatives and false positives and the priors were set equal forthe two states. No penalties were assessed for missing data, however themarker genes selected have strong representation across the dataset.10-fold cross validation was used to test the model. Settings notspecified remained at the default values.

The model shown in FIG. 7B is based on decisions about expression valuesat three nodes, each a different marker gene. The cost assigned to thismodel is 0.292, based on the priors being equal, the costs set to 1 foreach type of error, and the results from the 10-fold cross validation.

In the training set, no rejection samples were misclassified(sensitivity=100%) and only 1 no-rejection sample was misclassified(specificity=94.4%). Following 10-fold cross validation, 2 rejectionsamples were misclassified (sensitivity=87.5%) and 3 no-rejectionsamples were misclassified (specificity=83.3%). The CART softwareassigns surrogate markers for each decision node.

These genes can be used alone or in association with other genes orvariables to build a diagnostic gene set or a classification algorithm.These genes can be used in association with known gene markers forrejection (such as those identified in the prior art) to provide adiagnostic algorithm.

Example 11 Amplification, Labeling and Hybridization of Total RNA to anOligonucleotide Microarray

Amplification, Labeling, Hybridization and Scanning

Samples consisting of at least 0.5 to 2 μg of intact total RNA werefurther processed for array hybridization. When available, 2 μg ofintact total RNA is used for amplification. Amplification and labelingof total RNA samples was performed in three successive enzymaticreactions. First, a single-stranded DNA copy of the RNA was made(hereinafter, “ss-cDNA”). Second, the ss-cDNA was used as a template forthe complementary DNA strand, producing double-stranded cDNA(hereinafter, “ds-cDNA, or cDNA”). Third, linear amplification wasperformed by in vitro transcription from a bacterial T₇ promoter. Duringthis step, fluorescent-conjugated nucleotides were incorporated into theamplified RNA (hereinafter, “aRNA”).

The first strand cDNA was produced using the Invitrogen kit (SuperscriptII). The first strand cDNA was produced in a reaction composed of 50 mMTris-HCl (pH 8.3), 75 mM KCl, and 3 mM MgCl₂ (1× First Strand Buffer,Invitrogen), 0.5 mM dGTP, 0.5 mM dATP, 0.5 mM dTTP, 0.5 mM dCTP, 10 mMDTT, 200 U reverse transcriptase (Superscript II, Invitrogen,#18064014), 15 U RNase inhibitor (RNAGuard, Amersham Pharmacia,#27-0815-01), 5 μM T7T24 primer(5′-GGCCAGTGAATTGTAATACGACTCACTATAGGGAGGCGGTTTTTTTTTTTTTTTTTTTTTTTT-3′),(SEQ ID NO:3105) and 0.5 to 2 μg of selected sample total RNA. Severalpurified, recombinant control mRNAs from the plant Arabidopsis thalianawere added to the reaction mixture: 2-20 pg of the following genes CAB,RCA, LTP4, NAC1, RCP1, XCP2, RBCL, LTP6, TIM, and PRKase (Stratagene,#252201, #252202, #252204, #252208, #252207, #252206, #252203, #252210respectively). The control RNAs allow the estimate of copy numbers forindividual mRNAs in the clinical sample because corresponding senseoligonucleotide probes for each of these plant genes are present on themicroarray. The final reaction volume of 20 μl was incubated at 42° C.for 90 min. For synthesis of the second cDNA strand, DNA polymerase andRNase were added to the previous reaction, bringing the final volume to150 μl. The previous contents were diluted and new substrates were addedto a final concentration of 20 mM Tris-HCl (pH 7.0) (Fisher Scientific,Pittsburgh, Pa. #BP1756-100), 90 mMKCl (Teknova, Half Moon Bay, Calif.,#0313-500), 4.6 mM MgCl₂ (Teknova, Half Moon Bay, Calif., #0304-500), 10mM (NH₄)₂SO₄ (Fisher Scientific #A702-500)(1× Second Strand buffer,Invitrogen), 0.266 mM dGTP, 0.266 mM daTP, 0.266 mM dTTP, 0.266 mM dCTP,40 U E. coli DNA polymerase (Invitrogen, #18010-025), and 2 U RNaseH(Invitrogen, #18021-014). The second strand synthesis took place at 16°C. for 150 minutes.

Following second-strand synthesis, the ds-cDNA was purified from theenzymes, dNTPs, and buffers before proceeding to amplification, usingphenol-chloroform extraction followed by ethanol precipitation of thecDNA in the presence of glycogen.

Alternatively, a silica-gel column is used to purify the cDNA (e.g.Qiaquick PCR cleanup from Qiagen, #28104). The volume of the columnpurified cDNA was reduced by ethanol precipitation in the presence ofglycogen in which the cDNA was collected by centrifugation at >10,000×gfor 30 minutes, the supernatant is aspirated, and 150 μl of 70% ethanol,30% water was added to wash the DNA pellet. Following centrifugation,the supernatant was removed, and residual ethanol was evaporated at roomtemperature. Alternatively, the volume of the column purified cDNA isreduce in a vacuum evaporator where the supernatant is reduce to a finalvolume of 7.4 μl.

Linear amplification of the cDNA was performed by in vitro transcriptionof the cDNA. The cDNA pellet from the step described above wasresuspended in 7.4 μl of water, and in vitro transcription reactionbuffer was added to a final volume of 20 μl containing 7.5 mM GTP, 7.5mM ATP, 7.5 mM TTP, 2.25 mM CTP, 1.025 mM Cy3-conjugated CTP (PerkinElmer; Boston, Mass., #NEL-580), 1× reaction buffer (Ambion, MegascriptKit, Austin, Tex. and #1334) and 1% T₇ polymerase enzyme mix (Ambion,Megascript Kit, Austin, Tex. and #1334). This reaction was incubated at37° C. overnight. Following in vitro transcription, the RNA was purifiedfrom the enzyme, buffers, and excess NTPs using the RNeasy kit fromQiagen (Valencia, Calif.; # 74106) as described in the vendor'sprotocol. A second elution step was performed and the two eluates werecombined for a final volume of 60 μl. RNA is quantified using an Agilent2100 bioanalyzer with the RNA 6000 nano LabChip. Reference RNA wasprepared as described above, except Cy5-CTP was incorporated instead ofCy3CTP. Reference RNA from five reactions, each reaction started with 2ug total RNA, was pooled together and quantitated as described above.

Hybridization to an Array

RNA was prepared for hybridization as follows: for an 18 mm×55 mm array,20 μg of amplified RNA (aRNA) was combined with 20 μg of reference aRNA.The combined sample and reference aRNA was concentrated by evaporatingthe water to 10 μl in a vacuum evaporator. The sample was fragmented byheating the sample at 95° C. for 30 minutes to fragment the RNA into50-200 bp pieces. Alternatively, the combined sample and reference aRNAwas concentrated by evaporating the water to 5 μl in a vacuumevaporator. Five μl of 20 mM zinc acetate was added to the aRNA and themix incubated at 60° C. for 10 minutes. Following fragmentation, 40 μlof hybridization buffer was added to achieve final concentrations of5×SSC and 0.20% SDS with 0.1 μg/ul of Cot-1 DNA (Invitrogen) as acompetitor DNA. The final hybridization mix was heated to 98° C., andthen reduced to 50° C. at 0.1° C. per second.

Alternatively, formamide is included in the hybridization mixture tolower the hybridization temperature.

The hybridization mixture was applied to a pre-heated 65° C. microarray,surface, covered with a glass coverslip (Corning, #2935-246), and placedon a pre-heated 65° C. hybridization chamber (Telechem, AHC-10). 15 ulof 5×SSC was placed in each of the reservoir in the hybridizationchamber and the chamber was sealed and placed in a water bath at 62° C.for overnight (16-20 hrs). Following incubation, the slides were washedin 2×SSC, 0.1% SDS for five minutes at 30° C., then in 2×SSC for fiveminutes at 30° C., then in 2×SSC for another five minutes at 30° C.,then in 0.2×SSC for two minutes at room temperature. The arrays werespun at 1000×g for 2 minutes to dry them. The dry microarrays are thenscanned by methods described above.

The microarrays were imaged on the Agilent (Palo Alto, Calif.) scannerG2565AA. The scan settings using the Agilent software were as follows:for the PMT Sensitivity (100% Red and 100% Green); Scan Resolution (10microns); red and green dye channels; used the default scan region forall slides in the carousel; using the largest scan region; scan date forInstrument ID; and barcode for Slide ID. The full image produced by theAgilent scanner was flipped, rotated, and split into two images (one foreach signal channel) using TIFFSplitter (Agilent, Palo Alto, Calif.).The two channels are the output at 532 nm (Cy3-labeled sample) and 633nm (Cy5-labeled R50). The individual images were loaded into GenePix 3.0(Axon Instruments, Union City, Calif.) for feature extraction, eachimage was assigned an excitation wavelength corresponding the fileopened; Red equals 633 nm and Green equals 532 nm The setting file (gal)was opened and the grid was laid onto the image so that each spot in thegrid overlapped with >50% of the feature. Then the GenePix software wasused to find the features without setting minimum threshold value for afeature. For features with low signal intensity, GenePix reports “notfound”. For all features, the diameter setting was adjusted to includeonly the feature if necessary.

The GenePix software determined the median pixel intensity for eachfeature (F_(i)) and the median pixel intensity of the local backgroundfor each feature (B_(i)) in both channels. The standard deviation(SDF_(i and) SDB_(i)) for each is also determined. Features for whichGenePix could not discriminate the feature from the background were“flagged” as described below.

Following feature extraction into a “.gpr” file, the header informationof the .gpr file was changed to carry accurate information into thedatabase. An Excel macro was written to include the followinginformation: Name of the original .tif image file, SlideID, Version ofthe feature extraction software, GenePix Array List file, GenePixSettings file, ScanID, Name of person who scanned the slide, Green PMTsetting, Red PMT setting, ExtractID (date .gpr file was created,formatted as yyyy.mm.dd-hh.mm.ss), Results file name (same as the .gprfile name), StorageCD, and Extraction comments.

Pre-Processing with Excel Templates

Following analysis of the image and extraction of the data, the datafrom each hybridization was pre-processed to extract data that wasentered into the database and subsequently used for analysis. Thecomplete GPR file produced by the feature extraction in GenePix wasimported into an excel file pre-processing template or processed using aAWK script. Both programs used the same processing logic and produceidentical results. The same excel template or AWK script was used toprocess each GPR file. The template performs a series of calculations onthe data to differentiate poor features from others and to combineduplicate or triplicate feature data into a single data point for eachprobe.

The data columns used in the pre-processing were: Oligo ID, F633 Median(median value from all the pixels in the feature for the Cy5 dye), B633Median (the median value of all the pixels in the local background ofthe selected feature for Cy5), B633 SD (the standard deviation of thevalues for the pixels in the local background of the selected featurefor Cy5), F532 Median (median value from all the pixels in the featurefor the Cy3 dye), B532 Median (the median value of all the pixels in thelocal background of the selected feature for Cy3), B532 SD (the standarddeviation of the values for the pixels in the local background of theselected feature for Cy3), and Flags. The GenePix Flags column containsthe flags set during feature extraction. “−75” indicates there were nofeatures printed on the array in that position, “−50” indicates thatGenePix could not differentiate the feature signal from the localbackground, and “−100” indicates that the user marked the feature asbad.

Once imported, the data associated with features with −75 flags was notused. Then the median of B633 SD and B532 SD were calculated over allfeatures with a flag value of “0”. The minimum values of B633 Median andB532 Median were identified, considering only those values associatedwith a flag value of “0”. For each feature, the signal to noise ratio(S/N) was calculated for both dyes by taking the fluorescence signalminus the local background (BGSS) and dividing it by the standarddeviation of the local background:

${S/N} = \frac{F_{i} - B_{i}}{{SDB}_{i}}$

If the S/N was less than 3, then an adjusted background-subtractedsignal was calculated as the fluorescence minus the minimum localbackground on the slide. An adjusted S/N was then calculated as theadjusted background subtracted signal divided by the median noise overall features for that channel. If the adjusted S/N was greater thanthree and the original S/N were less than three, a flag of 25 was setfor the Cy5 channel, a flag of 23 was set for the Cy3 channel, and ifboth met these criteria, then a flag of 28 was set. If both the adjustedS/N and the original S/N were less than three, then a flag of 65 was setfor Cy5, 63 set for Cy3, and 68 set if both dye channels had an adjustedS/N less than three. All signal to noise calculations, adjustedbackground-subtracted signal, and adjusted S/N were calculated for eachdye channel. If the BGSS value was greater than or equal to 64000, aflag was set to indicate saturation; 55 for Cy5, 53 for Cy3, 58 forboth.

The BGSS used for further calculations was the original BGSS if theoriginal S/N was greater than or equal to three. If the original S/Nratio was less than three and the adjusted S/N ratio was greater than orequal to three, then the adjusted BGSS was used. If the adjusted S/Nratio was less than three, then the adjusted BGSS was used, but withknowledge of the flag status.

To facilitate comparison among arrays, the Cy3 and Cy5 data were scaled.The log of the ratio of Green/Red was determined for all features. Themedian log ratio value for good features (Flags 0, 23, 25, 28, 63) wasdetermined. The feature values were scaled using the following formula:Log_Scaled_Feature_Ratio=Log_Feature_Ratio−Median_Log_Ratio.

The flag setting for each feature was used to determine the expressionratio for each probe, a choice of one, two or three features. If allfeatures had flag settings in the same category (categories=negatives, 0to 28, 53-58, and 63-68), then the average of the three scaled, anti logfeature ratios was calculated If the three features did not have flagsin the same category, then the feature or features with the best qualityflags were used (0>25>23>28>55>53>58>65>63>68). Features with negativeflags were never used. When the best flags were two or three features inthe same category, the anti log average was used. If a single featurehad a better flag category than the other two then the anti log of thatfeature ratio was used.

Once the probe expression ratios were calculated from the one, two, orthree features, the log of the scaled, averaged ratios was taken asdescribed below and stored for use in analyzing the data. Whicheverfeatures were used to calculate the probe value, the flag from thosefeatures was carried forward and stored as the flag value for thatprobe. 2 different data sets can be used for analysis. Flagged data usesall values, including those with flags. Filtered data sets are createdby removing flagged data from the set before analysis.

Example 12 Real-Time PCR Validation of Array Expression Results

Leukocyte microarray gene expression was used to discover expressionmarkers and diagnostic gene sets for clinical outcomes. It is desirableto validate the gene expression results for each gene using a moresensitive and quantitative technology such as real-time PCR. Further, itis possible for the diagnostic nucleotide sets to be implemented as adiagnostic test as a real-time PCR panel. Alternatively, thequantitative information provided by real-time PCR validation can beused to design a diagnostic test using any alternative quantitative orsemi-quantitative gene expression technology. To validate the results ofthe microarray experiments we used real-time, or kinetic, PCR. In thistype of experiment the amplification product is measured during the PCRreaction. This enables the researcher to observe the amplificationbefore any reagent becomes rate limiting for amplification. In kineticPCR the measurement is of C_(T) (threshold cycle) or C_(P) (crossingpoint). This measurement (C_(T)=C_(P)) is the point at which anamplification curve crosses a threshold fluorescence value. Thethreshold is set to a point within the area where all of the reactionswere in their linear phase of amplification. When measuring C_(T), alower C_(T) value is indicative of a higher amount of starting materialsince an earlier cycle number means the threshold was crossed morequickly.

Several fluorescence methodologies are available to measureamplification product in real-time PCR. Taqman (Applied BioSystems,Foster City, Calif.) uses fluorescence resonance energy transfer (FRET)to inhibit signal from a probe until the probe is degraded by thesequence specific binding and Taq 3′ exonuclease activity. MolecularBeacons (Stratagene, La Jolla, Calif.) also use FRET technology, wherebythe fluorescence is measured when a hairpin structure is relaxed by thespecific probe binding to the amplified DNA. The third commonly usedchemistry is Sybr Green, a DNA-binding dye (Molecular Probes, Eugene,Oreg.). The more amplified product that is produced, the higher thesignal. The Sybr Green method is sensitive to non-specific amplificationproducts, increasing the importance of primer design and selection.Other detection chemistries can also been used, such as ethedium bromideor other DNA-binding dyes and many modifications of the fluorescentdye/quencher dye Taqman chemistry.

Sample Prep and cDNA Synthesis

The inputs for real time PCR reaction are gene-specific primers, cDNAfrom specific patient samples, and standard reagents. The cDNA wasproduced from mononuclear RNA (prepared as in example 2) or whole bloodRNA by reverse transcription using Oligo dT primers (Invitrogen,18418-012) and random hexamers (Invitrogen, 48190-011) at a finalconcentration of 0.5 ng/μl and 3 ng/μl respectively. For the firststrand reaction mix, 0.5 μg of mononuclear total RNA or 2 μg of wholeblood RNA and 1 μl of the Oligo dT/Random Hexamer Mix, were added towater to a final volume of 11.5 μl. The sample mix was then placed at70° C. for 10 minutes. Following the 70° C. incubation, the samples werechilled on ice, spun down, and 88.5 μl of first strand buffer mixdispensed into the reaction tube. The final first strand buffer mixproduced final concentrations of 1× first strand buffer (Invitrogen,Y00146, Carlsbad, Calif.), 10 mM DTT (Invitrogen, Y00147), 0.5 mM daTP(NEB, N0440S, Beverly, Mass.), 0.5 mM dGTP (NEB, N0442S), 0.5 mM dTTP(NEB, N0443S), 0.5 mM dCTP (NEB, N0441S), 200U of reverse transcriptase(Superscript II, Invitrogen, 18064-014), and 18U of RNase inhibitor(RNAGaurd Amersham Pharmacia, 27-0815-01, Piscataway, N.J.). Thereaction was incubated at 42° C. for 90 minutes. After incubation theenzyme was heat inactivated at 70° C. for 15 minutes, 2 U of RNAse Hadded to the reaction tube, and incubated at 37° C. for 20 minutes.

Primer Design

Two methods were used to design primers. The first was to use thesoftware, Primer Express™ and recommendations for primer design that areprovided with the GeneAmp® 7700 Sequence Detection System supplied byApplied BioSystems (Foster City, Calif.). The second method used todesign primers was the PRIMER3 ver 0.9 program that is available fromthe Whitehead Research Institute, Cambridge, Mass. at the WhiteheadResearch web site. The program can also be accessed on the World WideWeb at the web site at the Massachusetts Institute of Technologywebsite. Primers and Taqman/hybridization probes were designed asdescribed below using both programs. The Primer Express literatureexplains that primers should be designed with a melting temperaturebetween 58 and 60 degrees C. while the Taqman probes should have amelting temperature of 68 to 70 under the salt conditions of thesupplied reagents. The salt concentration is fixed in the software.Primers should be between 15 and 30 basepairs long. The primers shouldproduce and amplicon in size between 50 and 150 base pairs, have a C-Gcontent between 20% and 80%, have no more than 4 identical base pairsnext to one another, and no more than 2 C's and G's in the last 5 basesof the 3′ end. The probe cannot have a G on the 5′ end and the strandwith the fewest G's should be used for the probe.

Primer3 has a large number of parameters. The defaults were used for allexcept for melting temperature and the optimal size of the amplicon wasset at 100 bases. One of the most critical is salt concentration as itaffects the melting temperature of the probes and primers. In order toproduce primers and probes with melting temperatures equivalent toPrimer Express, a number of primers and probes designed by PrimerExpress were examined using PRIMER3. Using a salt concentration of 50 mMthese primers had an average melting temperature of 3.7 degrees higherthan predicted by Primer Express. In order to design primers and probeswith equivalent melting temperatures as Primer Express using PRIMER3, amelting temperature of 62.7 plus/minus 1.0 degree was used in PRIMER3for primers and 72.7 plus/minus 1.0 degrees for probes with a saltconcentration of 50 mM.

The C source code for Primer3 was downloaded and complied on a SunEnterprise 250 server using the GCC complier. The program was then usedfrom the command line using a input file that contained the sequence forwhich we wanted to design primers and probes along with the inputparameters as described by help files that accompany the software. Usingscripting it was possible to input a number of sequences andautomatically generate a number of possible probes and primers.

Primers for β-Actin (Beta Actin, Genbank Locus: NM_(—)001101) and β-GUS:glucuronidase, beta, (GUSB, Genbank Locus: NM_(—)000181), two referencegenes, were designed using both methods and are shown here as examples:

The first step was to mask out repetitive sequences found in the mRNAsequences using RepeatMasker program that can be accessed at: the website University of Washington Genome RepeatMasker website. (Smit, A. F.A. & Green, P.).

The last 500 basepairs on the last 3′ end of masked sequence was thensubmitted to PRIMER3 using the following exemplary input sequences:

PRIMER_SEQUENCE_ID=>ACTB Beta Actin (SEQID 3083) SEQUENCE =TTGGCTTGACTCAGGATTTAAAAACTGGAACGGTGAAGGTGACAGCAGTCGGTTGGACGAGCATCCCCCAAAGTTCACAATGTGGCCGAGGACTTTGATTGCACATTGTTGTTTTTTAATAGTCATTCCAAATATGAGATGCATTGTTACAGGAAGTCCCTTGCCATCCTAAAAGCACCCCACTTCTCTCTAAGGAGAATGGCCCAGTCCTCTCCCAAGTCCACACAGGGGAGGGATAGCATTGCTTTCGTGTAAATTATGTAATGCAAAATTTTTTTAATCTTCGCCTTAATCTTTTTTATTTTGTTTTATTTTGAATGATGAGCCTTCGTGCCCCCCCTTCCCCCTTTTTTCCCCCAACTTGAGATGTATGAAGGCTTTTGGTCTCCCTGGGAGTGGGTGGAGGCAGCCGGGCTTACCTGTACACTGACTTGAGACCAGTTGAATAAAAGTGCACACCTTAPRIMER_SEQUENCE_ID=>GUSB (SEQID 3084) SEQUENCE =GAAGAGTACCAGAAAAGTCTGCTAGAGCAGTACCATCTGGGTCTGGATCAAAAACGCAGAAAATATGTGGTTGGAGAGCTCATTTGGAATTTTGCCGATTTCATGACTGAACAGTCACCGACGAGAGTGCTGGGGAATAAAAAGGGGATCTTCACTCGGCAGAGACAACCAAAAAGTGCAGCGTTCCTTTTGCGAGAGAGATACTGGAAGATTGCCAATGAAACCAGGTATCCCCACTCAGTAGCCAAGTCACAATGTTTGGAAAACAGCCCGTTTACTTGAGCAAGACTGATACCACCTGCGTGTCCCTTCCTCCCCGAGTCAGGGCGACTTCCACAGCAGCAGAACAAGTGCCTCCTGGACTGTTCACGGCAGACCAGAACGTTTCTGGCCTGGGTTTTGTGGTCATCTATTCTAGCAGGGAACACTAAAGGTGGAAATAAAAGATTTTCTATTATGGAAATAAAGAGTTGGCATGAAAGTCGCTACTG

After running PRIMER3, 100 sets of primers and probes were generated forACTB and GUSB. From this set, nested primers were chosen based onwhether both left primers could be paired with both right primers and asingle Taqman probe could be used on an insert of the correct size. Withmore experience we have decided not use the mix and match approach toprimer selection and just use several of the top pairs of predictedprimers.

For ACTB this turned out to be: Forward 75 CACAATGTGGCCGAGGACTT, (SEQID3085) Forward 80 TGTGGCCGAGGACTTTGATT, (SEQID 3086) Reverse 178TGGCTTTTAGGATGGCAAGG, (SEQID 3087) and Reverse 168 GGGGGCTTAGTTTGCTTCCT.(SEQID 3088)

Upon testing, the F75 and R178 pair worked best.

For GUSB the following primers were chosen: Forward 59AAGTGCAGCGTTCCTTTTGC, (SEQID 3089) Forward 65 AGCGTTCCTTTTGCGAGAGA,(SEQID 3090) Reverse 158 CGGGCTGTTTTCCAAACATT, (SEQID 3091) and Reverse197 GAAGGGACACGCAGGTGGTA. (SEQID 3092)

No combination of these GUSB pairs worked well.

In addition to the primer pairs above, Primer Express predicted thefollowing primers for GUSB: Forward 178 TACCACCTGCGTGTCCCTTC (SEQID3093) and Reverse 242 GAGGCACTTGTTCTGCTGCTG (SEQID 3094). This pair ofprimers worked to amplify the GUSB mRNA.

The parameters used to predict these primers in Primer Express were:

Primer Tm: min 58, Max=60, opt 59, max difference=2 degrees

Primer GC: min=20% Max=80% no 3′ G/C clamp

Primer: Length: min=9 max=40 opt=20

Amplicon: min Tm=0 max Tm=85

min=50 bp max=150 bp

Probe: Tm 10 degrees>primers, do not begin with a G on 5′ end

Other: max base pair repeat=3

max number of ambiguous residues=0

secondary structure: max consecutive bp=4, max total bp=8

Uniqueness: max consecutive match=9

max % match=75

max 3′ consecutive match=7

Granzyme B is a marker of transplant rejection.

For Granzyme B the following sequence (NM_(—)004131) (SEQID 3096) wasused as input for Primer3:

GGGGACTCTGGAGGCCCTCTTGTGTGTAACAAGGTGGCCCAGGGCATTGTCTCCTATGGACGAAACAATGGCATGCCTCCACGAGCCTGCACCAAAGTCTCAAGCTTTGTACACTGGATAAAGAAAACCATGAAACGCTACTAACTACAGGAAGCAAACTAAGCCCCCGCTGTAATGAAACACCTTCTCTGGAGCCAAGTCCAGATTTACACTGGGAGAGGTGCCAGCAACTGAATAAATACCTCTCCCAGTGTAAATCTGGAGCCAAGTCCAGATTTACACTGGGAGAGGTGCCAGCAACTGAATAAATACCTCTTAGCTGAGTGG

For Granzyme B the following primers were chosen for testing: Forward 81ACGAGCCTGCACCAAAGTCT (SEQID 3097) Forward 63 AAACAATGGCATGCCTCCAC (SEQID3098) Reverse 178 TCATTACAGCGGGGGCTTAG (SEQID 3099) Reverse 168GGGGGCTTAGTTTGCTTCCT (SEQID 3100)

Testing demonstrated that F81 and R178 worked well.

Using this approach, primers were designed for all the genes that wereshown to have expression patterns that correlated with allograftrejection. These primer pairs are shown in Table 2, Table 8, and areadded to the sequence listing. Primers can be designed from any regionof a target gene using this approach.

Primer Endpoint Testing

Primers were first tested to examine whether they would produce thecorrect size product without non-specific amplification. The standardreal-time PCR protocol was used without the Rox and Sybr green dyes.Each primer pair was tested on cDNA made from universal mononuclearleukocyte reference RNA that was produced from 50 individuals asdescribed in Example 3 (R50).

The PCR reaction consisted of 1× RealTime PCR Buffer (Ambion, Austin,Tex.), 2 mM MgCl2 (Applied BioSystems, B02953), 0.2 mM dATP (NEB), 0.2mM dTTP (NEB), 0.2 mM dCTP (NEB), 0.2 mM dGTP (NEB), 625U AmpliTaq Gold(Applied BioSystems, Foster City, Calif.), 0.3 μM of each primer to beused (Sigma Genosys, The Woodlands, Tex.), 5 μl of the R50reverse-transcription reaction and water to a final volume of 19 μl.

Following 40 cycles of PCR, 10 microliters of each product was combinedwith Sybr green at a final dilution of 1:72,000. Melt curves for eachPCR product were determined on an ABI 7900 (Applied BioSystems, FosterCity, Calif.), and primer pairs yielding a product with one clean peakwere chosen for further analysis. One microliter of the product fromthese primer pairs was examined by agarose gel electrophoresis on anAgilent Bioanalyzer, DNA 1000 chip (Palo Alto, Calif.). Results for 2genes are shown in FIG. 9. From the primer design and the sequence ofthe target gene, one can calculate the expected size of the amplifiedDNA product. Only primer pairs with amplification of the desired productand minimal amplification of contaminants were used for real-time PCR.Primers that produced multiple products of different sizes are likelynot specific for the gene of interest and may amplify multiple genes orchromosomal loci.

Primer Optimization/Efficiency

Once primers passed the end-point PCR, the primers were tested todetermine the efficiency of the reaction in a real-time PCR reaction.cDNA was synthesized from starting total RNA as described above. A setof 5 serial dilutions of the R50 reverse-transcribed cDNA (as describedabove) were made in water: 1:10, 1:20, 1:40, 1:80, and 1:160.

The Sybr Green real-time PCR reaction was performed using the Taqman PCRReagent kit (Applied BioSystems, Foster City, Calif., N808-0228). Amaster mix was made that consisted of all reagents except the primes andtemplate. The final concentration of all ingredients in the reaction was1× Taqman Buffer A (Applied BioSystems), 2 mM MgCl2 (AppliedBioSystems), 200 μM dATP (Applied BioSystems), 200 μM dCTP (AppliedBioSystems), 200 μM dGTP (Applied BioSystems), 400 μM dUTP (AppliedBioSystems), 1:400,000 diluted Sybr Green dye (Molecular Probes), 1.25UAmpliTaq Gold (Applied BioSystems). The PCR master mix was dispensedinto two, light-tight tubes. Each β-Actin primer F75 and R178(Sigma-Genosys, The Woodlands, Tex.), was added to one tube of PCRmaster mix and Each β-GUS primer F178 and R242 (Sigma-Genosys), wasadded to the other tube of PCR master mix to a final primerconcentration of 300 nM. 45 μl of the β-Actin or β-GUS master mix wasdispensed into wells, in a 96-well plate (Applied BioSystems). 5 μl ofthe template dilution series was dispensed into triplicate wells foreach primer. The reaction was run on an ABI 7900 Sequence DetectionSystem (Applied BioSystems) with the following conditions: 10 min. at95° C.; 40 cycles of 95° C. for 15 sec, 60° C. for 1 min; followed by adisassociation curve starting at 50° C. and ending at 95° C.

The Sequence Detection System v2.0 software was used to analyze thefluorescent signal from each well. The high end of the baseline wasadjusted to between 8 and 20 cycles to reduce the impact on any datacurves, yet be as high as possible to reduce baseline drift. A thresholdvalue was selected that allowed the majority of the amplification curvesto cross the threshold during the linear phase of amplification. Thedisassociation curve for each well was compared to other wells for thatmarker. This comparison allowed identification of “bad” wells, thosethat did not amplify, that amplified the wrong size product, or thatamplified multiple products. The cycle number at which eachamplification curve crossed the threshold (C_(T)) was recorded and thefile transferred to MS Excel for further analysis. The C_(T) values fortriplicate wells were averaged. The data were plotted as a function ofthe log₁₀ of the calculated starting concentration of RNA. The startingRNA concentration for each cDNA dilution was determined based on theoriginal amount of RNA used in the RT reaction, the dilution of the RTreaction, and the amount used (5 μl) in the real-time PCR reaction. Foreach gene, a linear regression line was plotted through all of thedilutions series points. The slope of the line was used to calculate theefficiency of the reaction for each primer set using the equation:E=10^((−1/scope))−1

Using this equation (Pfaffl 2001, Applied Biosystems User Bulletin #2),the efficiency for these β-actin primers is 1.28 and the efficiency forthese β-GUS primers is 1.14 (FIG. 10). This efficiency was used whencomparing the expression levels among multiple genes and multiplesamples. This same method was used to calculate reaction efficiency forprimer pairs for each gene studied. A primer pair was consideredsuccessful if the efficiency was reproducibly determined to be between0.7 and 2.4.

SYBR-Green Assays

Once markers passed the Primer Efficiency QPCR (as stated above), theywere used in real-time PCR assays. Patient RNA samples werereverse-transcribed to cDNA (as described above) and 1:10 dilutions madein water. In addition to the patient samples, a no template control(NTC) and a pooled reference RNA (see example 3) described in wereincluded on every plate.

The Sybr Green real-time PCR reaction was performed using the TaqmanCore PCR Reagent kit (Applied BioSystems, Foster City, Calif.,N808-0228). A master mix was made that consisted of all reagents exceptthe primers and template. The final concentration of all ingredients inthe reaction was 1× Taqman Buffer A (Applied BioSystems), 2 mM MgCl2(Applied BioSystems), 200 μM dATP (Applied BioSystems), 200 μM dCTP(Applied BioSystems), 200 μM dGTP (Applied BioSystems), 400 μM dUTP(Applied BioSystems), 1:400,000 diluted Sybr Green dye (MolecularProbes), 1.25U AmpliTaq Gold (Applied BioSystems). The PCR master mixwas aliquoted into eight light-tight tubes, one for each marker to beexamined across a set of samples. The optimized primer pair for eachmarker was then added to the PCR master mix to a final primerconcentration of 300 nM. 18 μl of the each marker master mix wasdispensed into wells in a 384 well plate (Applied BioSystems). 2 μl ofthe 1:10 diluted control or patient cDNA sample was dispensed intotriplicate wells for each primer pair. The reaction was run on an ABI7900 Sequence Detection System (Applied BioSystems) using the cyclingconditions described above.

The Sequence Detection System v2.0 software (Applied BioSystems) wasused to analyze the fluorescent signal from each well. The high end ofthe baseline was adjusted to between 8 and 20 cycles to reduce theimpact on any data curves, yet be as high as possible to reduce baselinedrift. A threshold value was selected that allowed the majority of theamplification curves to cross the threshold during the linear phase ofamplification. The disassociation curve for each well was compared toother wells for that marker. This comparison allowed identification of“bad” wells, those that did not amplify, that amplified the wrong sizeproduct, or that amplified multiple products. The cycle number at whicheach amplification curve crossed the threshold (C_(T)) was recorded andthe file transferred to MS Excel for further analysis. The C_(T) valuerepresenting any well identified as bad by analysis of disassociationcurves was deleted. The C_(T) values for triplicate wells were averaged.A standard deviation (Stdev) and a coefficient of variation (CV) werecalculated for the triplicate wells. If the CV was greater than 2, anoutlier among the three wells was identified and deleted. Then theaverage was re-calculated. In each plate, ΔC_(T) was calculated for eachmarker-control combination by subtracting the average C_(T) of thetarget marker from the average C_(T) of the control (β-Actin or β-GUS).The expression relative to the control marker was calculated by takingtwo to the power of the ΔC_(T) of the target marker. For example,expression relative to β-Actin was calculated by the equation:ErA=2^((C) ^(T,Actin) ^(−C) ^(T,arg et))

All plates were run in duplicate and analyzed in the same manner. Thepercent variation was determined for each sample-marker combination(relative expression) by taking the absolute value of the value of theRE for the second plate from the RE for the first plate, and dividingthat by the average. If more than 25% of the variation calculations on aplate are greater than 50%, then a third plate was run.

Taqman Protocol

Real-time PCR assays were also done using Taqman PCR chemistry.

The Taqman real-time PCR reaction was performed using the TaqmanUniversal PCR Master Mix (Applied BioSystems, Foster City, Calif.,#4324018). The master mix was aliquoted into eight, light-tight tubes,one for each marker. The optimized primer pair for each marker was thenadded to the correctly labeled tube of PCR master mix. A FAM/TAMRAdual-labeled Taqman probe (Biosearch Technologies, Navoto, Calif.,DLO-FT-2) was then added to the correctly labeled tube of PCR mastermix. Alternatively, different combinations of fluorescent reporter dyesand quenchers can be used such that the absorption wavelength for thequencher matches the emission wavelength for the reporter, as shown inTable 5. 18 μl of the each marker master mix was dispensed into a 384well plate (Applied BioSystems). 2 μl of the template sample wasdispensed into triplicate wells for each primer pair. The finalconcentration of each reagent was: 1× TaqMan Universal PCR Master Mix,300 nM each primer, 0.25 nM probe, 2 μl 1:10 diluted template. Thereaction was run on an ABI 7900 Sequence Detection System (AppliedBiosystems) using standard conditions (95° C. for 10 min., 40 cycles of95° C. for 15 sec, 60° C. for 1 min.).

The Sequence Detector v2.0 software (Applied BioSystems) was used toanalyze the fluorescent signal from each well. The high end of thebaseline was adjusted to between 8 and 20 cycles to reduce the impact onany data curves, yet be as high as possible to reduce baseline drift. Athreshold value was selected that allowed most of the amplificationcurves to cross the threshold during the linear phase of amplification.The cycle number at which each amplification curve crossed the threshold(C_(T)) was recorded and the file transferred to MS Excel for furtheranalysis. The C_(T) values for triplicate wells were averaged. The C_(T)values for triplicate wells were averaged. A standard deviation (Stdev)and a coefficient of variation (CV) were calculated for the triplicatewells. If the CV was greater than 2, an outlier among the three wellswas identified and deleted. Then the average was re-calculated. In eachplate, ΔC_(T) was calculated for each marker-control combination bysubtracting the average C_(T) of the target marker from the averageC_(T) of the control (β-Actin or β-GUS). The expression relative to thecontrol marker was calculated by taking two to the power of the ΔC_(T)of the target marker. All plates were run in duplicate and analyzed inthe same manner. The percent variation was determined for eachsample-marker combination (relative expression) by taking the absolutevalue of the value of the RE for the second plate from the RE for thefirst plate, and dividing that by the average. If more than 25% of thevariation calculations on a plate are greater than 50%, then a thirdplate was run.

Bi-Plexing

Variation of real-time PCR assays can arise from unequal amounts of RNAstarting material between reactions. In some assays, to reducevariation, the control gene amplification was included in the samereaction well as the target gene. To differentiate the signal from thetwo genes, different fluorescent dyes were used for the control gene.β-Actin was used as the control gene and the TaqMan probe used waslabeled with the fluorescent dye VIC and the quencher TAMRA (BiosearchTechnologies, Navoto, Calif., DLO-FT-2). Alternatively, othercombinations of fluorescent reporter dyes and quenchers (Table 5) can beused as long as the emission wavelength of the reporter for the controlgene is sufficiently different from the wavelength of the reporter dyeused for the target. The control gene primers and probe were used atlimiting concentrations in the reaction (150 nM primers and 0.125 nMprobe) to ensure that there were enough reagents to amplify the targetmarker. The plates were run under the same protocol and the data areanalyzed in the same way, but with a separate baseline and threshold forthe VIC signal. Outliers were removed as above from both the FAM and VICsignal channels. The expression relative to control was calculated asabove, using the VIC signal from the control gene.

Absolute Quantitation

Instead of calculating the expression relative to a reference marker, anabsolute quantitation can be performed using real-time PCR. To determinethe absolute quantity of each marker, a standard curve is constructedusing serial dilutions from a known amount of template for each markeron the plate. The standard curve may be made using cloned genes purifiedfrom bacteria or using synthetic complimentary oligonucleotides. Ineither case, a dilution series that covers the expected range ofexpression is used as template in a series of wells in the plate. Fromthe average C_(T) values for these known amounts of template a standardcurve can be plotted. From this curve the C_(T) values for the unknownsare used to identify the starting concentration of cDNA. These absolutequantities can be compared between disease classes (i.e. rejection vs.no-rejection) or can be taken as expression relative to a control geneto correct for variation among samples in sample collection, RNApurification and quantification, cDNA synthesis, and the PCRamplification.

Cell Type Specific Expression

Some markers are expressed only in specific types of cells. Thesemarkers may be useful markers for differentiation of rejection samplesfrom no-rejection samples or may be used to identify differentialexpression of other markers in a single cell type. A specific marker forcytotoxic T-lymphocytes (such as CD8) can be used to identifydifferences in cell proportions in the sample. Other markers that areknown to be expressed in this cell type can be compared to the level ofCD8 to indicate differential gene expression within CD8 T-cells.

Control Genes for PCR

As discussed above, PCR expression measurements can be made as eitherabsolute quantification of gene expression using a standard curve orrelative expression of a gene of interest compared to a control gene. Inthe latter case, the gene of interest and the control gene are measuredin the same sample. This can be done in separate reactions or in thesame reaction (biplex format, see above). In either case, the finalmeasurement for expression of a gene is expressed as a ratio of geneexpression to control gene expression. It is important for a controlgene to be constitutively expressed in the target tissue of interest andhave minimal variation in expression on a per cell basis betweenindividuals or between samples derived from an individual. If the genehas this type of expression behavior, the relative expression ratio willhelp correct for variability in the amount of sample RNA used in anassay. In addition, an ideal control gene has a high level of expressionin the sample of interest compared to the genes being assayed. This isimportant if the gene of interest and control gene are used in a biplexformat. The assay is set up so that the control gene reaches itsthreshold Ct value early and its amplification is limited by primers sothat it does not compete for limiting reagents with the gene ofinterest.

To identify an ideal control gene for an assay, a number of genes weretested for variability between samples and expression in bothmononuclear RNA samples and whole blood RNA samples using the RNAprocurement and preparation methods and real-time PCR assays describedabove. 6 whole-blood and 6 mononuclear RNA samples from transplantrecipients were tested. The intensity levels and variability of eachgene in duplicate experiments on both sample types are shown in FIG. 11.Based on criteria of low variability and high expression across samples,β-actin, 18s, GAPDH, b2microglobulin were found to be good examples ofcontrol genes for the PAX samples. A single control gene may beincorporated as an internal biplex control is assays.

Controlling for Variation in Real Time PCR

Due to differences in reagents, experimenters, and preparation methods,and the variability of pipetting steps, there is significantplate-to-plate variation in real-time PCR experiments. This variationcan be reduced by automation (to reduce variability and error), reagentlot quality control, and optimal data handling. However, the results onreplicate plates are still likely to be different since they are run inthe machine at different times.

Variation can also enter in data extraction and analysis. Real-time PCRresults are measured as the time (measured in PCR cycles) at which thefluorescence intensity (□Rn in Applied Biosystems SDS v2.1 software)crosses a user-determined threshold (CT). When performing relativequantification, the CT value for the target gene is subtracted from theCT value for a control gene. This difference, called ΔCT, is the valuecompared among experiments to determine whether there is a differencebetween samples. Variation in setting the threshold can introduceadditional error. This is especially true in the duplexed experimentalformat, where both the target gene and the control gene are measured inthe same reaction tube. Duplexing is performed using dyes specific toeach of the two genes. Since two different fluorescent dyes are used onthe plate, two different thresholds are set. Both of these thresholdscontribute to each ΔCT. Slight differences in the each dye's thresholdsettings (relative to the other dye) from one plate to the next can havesignificant effects on the ΔCT.

There are several methods for setting the threshold for a PCR plate.Older versions of SDS software (Applied Biosystems) determine theaverage baseline fluorescence for the plate and the standard deviationof the baseline. The threshold is set to 10× the standard deviation ofthe baseline. In SDS 2.0 the users must set the baseline by themselves.Software from other machine manufacturers either requires the user toset the threshold themselves or uses different algorithms. The latestversion of the SDS software (SDS 2.1) contains Automatic baseline andthreshold setting. The software sets the baseline separately for eachwell on the plate using the ΔRn at cycles preceding detectable levels.Variability among plates is dependent on reproducible threshold setting.This requires a mathematical or experimental data driven thresholdsetting protocol. Reproducibly setting the threshold according to astandard formula will minimize variation that might be introduced in thethreshold setting process. Additionally, there may be experimentalvariation among plates that can be reduced by setting the threshold to acomponent of the data. We have developed a system that uses a set ofreactions on each plate that are called the threshold calibrator (TCb).The TCb wells are used to set the threshold on all plates.

1. The TCb wells contain a template, primers, and probes that are commonamong all plates within an experiment.

2. The threshold is set within the minimum threshold and maximumthreshold determined above.

3. The threshold is set to a value in this range that results in theaverage CT value for the TCb wells to be the same on all plates.

These methods were used to derive the primers depicted in Table 2C.

Example 13 Real-Time PCR Expression Markers of Acute Allograft Rejection

In examples 14 and 16, genes were identified as useful markers ofcardiac and renal allograft rejection using microarrays. Some genesidentified through these studies are listed in Table 2. In order tovalidate these findings, obtain a more precise measurement of expressionlevels and develop PCR reagents for diagnostic testing, real-time PCRassays were performed on samples from allograft recipients using primersto the identified genes. Some gene specific PCR primers were developedand tested for all genes in Table 2A as described in example 12. Someprimers are listed in Table 2C and the sequence listing. These primerswere used to measure expression of the genes relative to β-actin orβ-gus in 69 mononuclear RNA samples obtained from cardiac allograftrecipients using Sybr green real-time PCR assays as described in example12. Each sample was associated with an ISHLT cardiac rejection biopsygrade. The samples were tested in 2 phases. In phase I, 14 Grade 0, 1Grade 1A, 3 Grade 2 and 9 Grade 3A samples were tested. In phase II, 19Grade 2, 4 Grade 1B, 4 Grade 2 and 15 Grade 3A samples were tested. Datawas analyzed for each phase individually and for the combined phase I+IIsample set. These data are summarized in Table 6.

The average fold change in expression between rejection (3A) and norejection (0) samples was calculated. A t-test was done to determine thesignificance with which each gene was differentially expressed betweenrejection and no rejection and a p-value was calculated. Genes with highaverage fold changes and low p-values are considered best candidates forfurther development as rejection markers. However, it is important tonote that a gene with a low average fold change and a high p-value maystill be a useful marker for rejection in some patients and may work aspart of a gene expression panel to diagnose rejection. These same PCRdata were used to create PCR gene expression panels for diagnosis ofacute rejection as discussed in example 17.

Non-parametric tests such as the Fisher Exact Test and Mann-Whitney Utest are useful for choosing useful markers. They assess the ability ofmarkers to discriminate between different classes as well as theirsignificance. For example, one could use the median of all samples(including both non-rejector and rejector samples) as a threshold andapply the Fisher Exact test to the numbers of rejectors andnon-rejectors above and below the threshold.

These methods were used to generate the data in Table 2D.

Example 14 Identification of Diagnostic Nucleotide Sets for Diagnosis ofCardiac Allograft Rejection Using Microarrays

Genes were identified which have expression patterns useful for thediagnosis and monitoring of acute cardiac allograft rejection. Further,sets of genes that work together in a diagnostic algorithm for allograftrejection were identified. Acute allograft rejection is a process thatoccurs in all solid organ transplantation including, heart, lung, liver,kidney, pancreas, pancreatic islet cell, intestine and others. Geneexpression markers of acute cardiac rejection may be useful fordiagnosis and monitoring of all allograft recipients. Patients, patientclinical data and patient samples used in the discovery of markers belowwere derived from a clinical study described in example 5.

The collected clinical data was used to define patient or sample groupsfor correlation of expression data. Patient groups were identified forcomparison. ° For example, a patient group that possesses a useful orinteresting clinical distinction, verses a patient group that does notpossess the distinction. Measures of cardiac allograft rejection werederived from the clinical data to divide patients (and patient samples)into groups with higher and lower rejection activity over some period oftime or at any one point in time. Such data were rejection grades asdetermined from histological reading of the cardiac biopsy specimens bya pathologist and data measuring progression of end-organ damage,including depressed left ventricular dysfunction (decreased cardiacoutput, decreased ejection fraction, clinical signs of low cardiacoutput) and usage of inotropic agents (Kobashigawa 1998).

Mononuclear RNA samples were collected and prepared from patients whohad recently undergone a cardiac allograft transplantation using theprotocol described in example 2. The allograft rejection status at thetime of sample collection was determined by examination of cardiacbiopsies as described in example 5 and as summarized here.

300 patient samples were included in the analysis. Each patient samplewas associated with a biopsy and other clinical data collected at thetime of the sample. The cardiac biopsies were graded by a pathologist atthe local center and by three centralized pathologists who read thebiopsy slides from all four local centers in a blinded manner. Biopsygrades included 0, 1A, 1B, 2, 3A, and 3B. No grade 4 rejection wasidentified. Dependent variables were developed based on these gradesusing the local center pathology reading, the reading of a centralizedand blinded pathologist, the highest of the readings, local orcentralized and a consensus grade derived from all pathologicalreadings. Samples were classified as no rejection or rejection in thefollowing ways: Grade 0 vs. Grades 14, Grades 0 and 1A vs. Grades 1B-4,Grade 0 vs. Grade 3A, Grade 0 vs. Grades 1B-4, and Grade 0 vs. Grades 1Band 3A-4. Grade 0 samples were selected such that they were notimmediately followed by an episode of acute rejection in the samepatient. Comparing Grade 0 samples to Grade 3A samples gives thegreatest difference between the rejection and no rejection groups onaverage.

Taking the highest of all pathologist readings has the effect ofremoving any sample from the no rejection class that was not a unanimousGrade 0. It also results in an increase in the number of rejectionsamples used in an analysis with the assumption that if a pathologistsaw features of rejection, the call was likely correct and the otherpathologists may have missed the finding. Many leading cardiacpathologists and clinicians believe that ISHLT grade 2 rejection doesnot represent significant acute rejection. Thus, for correlationanalysis, exclusion of Grade 2 samples may be warranted. Clinical datawere also used to determine criteria for including samples in theanalysis. For example, a patient with an active infection or in theearly post-transplant period (ongoing surgical inflammation) might haveimmune activation unrelated to rejection and thus be difficult toidentify as patients without rejection. The strictest inclusion criteriarequired that samples be from patients who did not have a bacterial orviral infection, were at least two weeks post cardiac transplant, wereasymptomatic and were not currently admitted to the hospital.

After preparation of RNA (example 2), amplification, labeling,hybridization, scanning, feature extraction and data processing weredone as described in Example 11, using the oligonucleotide microarraysdescribed in Example 9. The resulting log ratio of expression of Cy3(patient sample)/Cy5 (R50 reference RNA) was used for analysis.

Significance analysis for microarrays (SAM, Tusher 2001, Example 15) wasused to discover genes that were differentially expressed between therejection and no-rejection groups. Many different combinations ofdependent variables, inclusion criteria, static/referenced, and datasubsets were used in SAM analysis to develop the primary lists of genessignificantly differentially expressed between rejection andno-rejection. As described in example 15, SAM assigns a false detectionrate to each gene identified as differentially expressed. The mostsignificant of these genes were identified. An exemplary analysis wasthe comparison of Grade 0 samples to Grade 3A-4 samples using SAM. Datafrom the all the pathological readings was used to identify consensusGrade 0 samples and samples with at least one reading of Grade 3A orabove. Using this definition of rejection and no rejection, expressionprofiles from rejection samples were compared to no rejection samplesusing SAM. The analysis identified 7 genes with a FDR of 1%, 15 genes@1.4%, 35 genes @3.9%. Many more genes were identified at higher FDRlevels.

In Table 7, a number of SAM analyses are summarized. In each case thehighest grade from the 3 pathologists was taken for analysis. Norejection and rejection classes are defined. Samples are either usedregardless of redundancy with respect to patients or a requirement ismade that only one sample is used per patient or per patient per class.The number of samples used in the analysis is given and the lowest FDRachieved is noted.

Some of the genes identified by SAM as candidate rejection markers arenoted in Table 2A and B. SAM chooses genes as significantly differentbased on the magnitude of the difference between the groups and thevariation among the samples within each group. It is important to notethat a gene which is not identified by SAM as differentially expressedbetween rejection and no rejection may still be a useful rejectionmarker because: 1. The microarray technology is not adequately sensitiveto detect all genes expressed at low levels. 2. A gene might be a usefulmember of a gene expression panel in that it is a useful rejectionmarker only in a subset of patients. This gene may not be significantlydifferentially expressed between all rejection and no rejection samples.

For the purposes of cross-validation of the results, the datasets werealso divided into subsets to compare analysis between two subsets ofroughly half of the data. The types of subsets constructed were asfollows. First half/second half subsets were the first half of thesamples and the second half of the samples from a dataset ordered bysample number. Odd/even subsets used the same source, a dataset orderedby sample number, but the odd subset consisted of every 2^(nd) samplestarting with the first and the even subset consisted of every 2^(nd)sample starting with the second sample, Center 14/other subsets were thesame datasets, divided by transplant hospital. The center 14 subsetconsisted of all samples from patients at center 14, while the othersubset consisted of all samples from the other three centers (12,13, and15). When a gene was found to be significantly differentially expressedin both sets of data, a higher priority was put on that gene fordevelopment of a diagnostic test. This was reflected in a “Array Score”value (Table 2B) that also considered the false detection rate for thegene and the importance of the gene in classification models (seeexample 17).

Alternatively one can divide samples into 10 equal parts and do 10-foldcross validation of the results of SAM.

Microarray data was also used to generate classification models fordiagnosis of rejection as described in example 17. Genes identifiedthrough classification models as useful in the diagnosis of rejectionare noted in Table 2B in the column “models”.

As genes were identified as useful rejection markers by microarraysignificance analysis, classification models, PCR analysis, or throughsearching the prior art, a variety of approaches were employed todiscover genes that had similar expression behavior (coexpression) tothe gene of interest. If a gene is a useful rejection marker, then agene that is identified as having similar expression behavior is alsolikely to be a useful rejection marker. Hierarchical clustering (Eisenet al. 1998, see example 15) was used to identify co-expressed genes forestablished rejection markers. Genes were identified from the nearestbranches of the clustering dendrogram. Gene expression profilesgenerated from 240 samples derived from transplant recipients weregenerated as described above. Hierarchical clustering was performed andco-expressed genes of rejection markers were identified. An example isshown in FIG. 12. SEQ ID NO:85 was shown to be significantlydifferentially expressed between rejection and no rejection using bothmicroarrays and PCR. Gene SEQ ID NO:3020 was identified by hierarchicalclustering as closely co-expressed with SEQ ID NO:85. In table 2B, genesidentified as co-expressed with established markers are identified assuch by listing the SEQ ID that they are co-expressed with in the columnlabeled “clusters”.

Some of the primers for real-time PCR validation were designed for eachof the marker genes as described in Example 12 and are listed in Table2C and the sequence listing. PCR expression measurements using theseprimers were used to validate array findings, more accurately measuredifferential gene expression and create PCR gene expression panels fordiagnosis of rejection as described in example 17.

Alternative methods of analyzing the data may involve 1) using thesample channel without normalization by the reference channel, 2) usingan intensity-dependent normalization based on the reference whichprovides a greater correction when the signal in the reference channelis large, 3) using the data without background subtraction orsubtracting an empirically derived function of the background intensityrather than the background itself.

These methods were used to identify genes listed in Table 2B.

Example 15 Correlation and Classification Analysis

After generation and processing of expression data sets from microarraysas described in Example 11, a log ratio value is used for mostsubsequent analysis. This is the logarithm of the expression ratio foreach gene between sample and universal reference. The processingalgorithm assigns a number of flags to data that are of low signal tonoise, saturated signal or are in some other way of low or uncertainquality. Correlation analysis can proceed with all the data (includingthe flagged data) or can be done on filtered data sets where the flaggeddata is removed from the set. Filtered data should have less variabilityand noise and may result in more significant or predictive results.Flagged data contains all information available and may allow discoveryof genes that are missed with the filtered data set. After filtering thedata for quality as described above and in example 11, missing data arecommon in microarray data sets. Some algorithms don't require completedata sets and can thus tolerate missing values. Other algorithms areoptimal with or require imputed values for missing data. Analysis ofdata sets with missing values can proceed by filtering all genes fromthe analysis that have more than 5%, 10%, 20%, 40%, 50%, 60% or other %of values missing across all samples in the analysis. Imputation of datafor missing values can be done by a variety of methods such as using therow mean, the column mean, the nearest neighbor or some other calculatednumber. Except when noted, default settings for filtering and imputationwere used to prepare the data for all analytical software packages.

In addition to expression data, clinical data are included in theanalysis. Continuous variables, such as the ejection fraction of theheart measured by echocardiography or the white blood cell count can beused for correlation analysis. Any piece of clinical data collected onstudy subjects can be used in a correlation or classification analysis.In some cases, it may be desirable to take the logarithm of the valuesbefore analysis. These variables can be included in an analysis alongwith gene expression values, in which case they are treated as another“gene”. Sets of markers can be discovered that work to diagnose apatient condition and these can include both genes and clinicalparameters. Categorical variables such as male or female can also beused as variables for correlation analysis. For example, the sex of apatient may be an important splitter for a classification tree.

Clinical data are used as supervising vectors (dependent variables) forthe significance or classification analysis of expression data. In thiscase, clinical data associated with the samples are used to dividesamples in to clinically meaningful diagnostic categories forcorrelation or classification analysis. For example, pathologicspecimens from kidney biopsies can be used to divide lupus patients intogroups with and without kidney disease. A third or more categories canalso be included (for example “unknown” or “not reported”). Aftergeneration of expression data and definition of supervising vectors,correlation, significance and classification analysis are used todetermine which set of genes and set of genes are most appropriate fordiagnosis and classification of patients and patient samples. Two maintypes of expression data analyses are commonly performed on theexpression data with differing results and purposes. The first issignificance analyses or analyses of difference. In this case, the goalof the analysis is to identify genes that are differentially expressedbetween sample groups and to assign a statistical confidence to thosegenes that are identified. These genes may be markers of the diseaseprocess in question and are further studied and developed as diagnostictools for the indication. The second major type of analysis isclassification analysis. While significance analysis identifiesindividual genes that are differentially expressed between samplegroups, classification analysis identifies gene sets and an algorithmfor their gene expression values that best distinguish sample (patient)groups. The resulting gene expression panel and algorithm can be used tocreate and implement a diagnostic test. The set of genes and thealgorithm for their use as a diagnostic tool are often referred toherein as a “model”. Individual markers can also be used to create agene expression diagnostic model. However, multiple genes (or gene sets)are often more useful and accurate diagnostic tools.

Significance Analysis for Microarrays (SAM)

Significance analysis for microarrays (SAM) (Tusher 2001) is a methodthrough which genes with a correlation between their expression valuesand the response vector are statistically discovered and assigned astatistical significance. The ratio of false significant to significantgenes is the False Discovery Rate (FDR). This means that for eachthreshold there are some number of genes that are called significant,and the FDR gives a confidence level for this claim. If a gene is calleddifferentially expressed between two classes by SAM, with a FDR of 5%,there is a 95% chance that the gene is actually differentially expressedbetween the classes. SAM will identify genes that are differentiallyexpressed between the classes. The algorithm selects genes with lowvariance within a class and large variance between classes. Thealgorithm may not identify genes that are useful in classification, butare not differentially expressed in many of the samples. For example, agene that is a useful marker for disease in women and not men, may notbe a highly significant marker in a SAM analysis, but may be useful aspart of a gene set for diagnosis of a multi-gene algorithm.

After generation of data from patient samples and definition ofcategories using clinical data as supervising vectors, SAM is used todetect genes that are likely to be differentially expressed between thegroupings. Those genes with the highest significance can be validated byreal-time PCR (Example 13) or can be used to build a classificationalgorithm as described here.

Classification

Classification algorithms are used to identify sets of genes andformulas for the expression levels of those genes that can be applied asdiagnostic and disease monitoring tests. The same classificationalgorithms can be applied to all types of expression and proteomic data,including microarray and PCR based expression data. Examples ofclassification models are given in example 17. The discussion belowdescribes the algorithms that were used and how they were used.

Classification and Regression Trees (CART) is a decision treeclassification algorithm (Breiman 1984). From gene expression and orother data, CART can develop a decision tree for the classification ofsamples. Each node on the decision tree involves a query about theexpression level of one or more genes or variables. Samples that areabove the threshold go down one branch of the decision tree and samplesthat are not go down the other branch. Genes from expression data setscan be selected for classification building with CART by significantdifferential expression in SAM analysis (or other significance test),identification by supervised tree-harvesting analysis, high fold changebetween sample groups, or known relevance to classification of thetarget diseases. In addition, clinical data can be used as independentvariables for CART that are of known importance to the clinical questionor are found to be significant predictors by multivariate analysis orsome other technique. CART identifies predictive variables and theirassociated decision rules for classification (diagnosis). CART alsoidentifies surrogates for each splitter (genes that are the next bestsubstitute for a useful gene in classification). Analysis is performedin CART by weighting misclassification costs to optimize desiredperformance of the assay. For example, it may be most important that thesensitivity of a test for a given diagnosis be >90%. CART models can bebuilt and tested using 10 fold cross-validation or v-fold crossvalidation (see below). CART works best with a smaller number ofvariables (5-50). Multiple Additive Regression Trees (Friedman, J H1999, MART) is similar to CART in that it is a classification algorithmthat builds decision trees to distinguish groups. MART builds numeroustrees for any classification problem and the resulting model involves acombination of the multiple trees. MART can select variables as it buildmodels and thus can be used on large data sets, such as those derivedfrom an 8000 gene microarray. Because MART uses a combination of manytrees and does not take too much information from any one tree, itresists over training. MART identifies a set of genes and an algorithmfor their use as a classifier.

A Nearest Shrunken Centroids Classifier can be applied to microarray orother data sets by the methods described by Tibshirani et al. 2002. Thisalgorithms also identified gene sets for classification and determinestheir 10 fold cross validation error rates for each class of samples.The algorithm determines the error rates for models of any size, fromone gene to all genes in the set. The error rates for either or bothsample classes can are minimized when a particular number of genes areused. When this gene number is determined, the algorithm associated withthe selected genes can be identified and employed as a classifier onprospective sample.

For each classification algorithm and for significance analysis, genesets and diagnostic algorithms that are built are tested by crossvalidation and prospective validation. Validation of the algorithm bythese means yields an estimate of the predictive value of the algorithmon the target population. There are many approaches, including a 10 foldcross validation analysis in which 10% of the training samples are leftout of the analysis and the classification algorithm is built with theremaining 90%. The 10% are then used as a test set for the algorithm.The process is repeated 10 times with 10% of the samples being left outas a test set each time. Through this analysis, one can derive a crossvalidation error which helps estimate the robustness of the algorithmfor use on prospective (test) samples. Any % of the samples can be leftout for cross validation (v-fold cross validation, LOOCV). When a geneset is established for a diagnosis with an acceptable cross validationerror, this set of genes is tested using samples that were not includedin the initial analysis (test samples). These samples may be taken fromarchives generated during the clinical study. Alternatively, a newprospective clinical study can be initiated, where samples are obtainedand the gene set is used to predict patient diagnoses.

Example 16 Acute Allograft Rejection: Biopsy Tissue Gene ExpressionProfiling

Acute allograft rejection involves activation of recipient leukocytesand infiltration into the rejecting organ. For example, CD8 T-cells areactivated by CD4 T-cells and enter the allograft where they destroygraft tissue. These activated, graft-associated leukocytes may reside inthe graft, die or exit the graft. Upon exiting, the cells can find theirway into the urine or blood (in the case of renal allografts), bile orblood (liver allografts) or blood (cardiac allografts). These activatedcells have specific gene expression patterns that can be measured usingmicroarrays, PCR or other methods. These gene expression patterns can bemeasured in the graft tissue (graft associated leukocytes), bloodleukocytes, urine leukocytes or stool/biliary leukocytes. Thus graftassociated leukocyte gene expression patterns are used to discovermarkers of activated leukocytes that can be measured outside the graftfor diagnostic testing.

Renal biopsy and cardiac biopsy tissue specimens were obtained for geneexpression profiling. The specimens were obtained at the time ofallograft biopsy and were preserved by flash freezing in liquid nitrogenusing standard approaches or immersion in an RNA stabilization reagentas per the manufacturers recommendation (RNAlater, Qiagen, Valencia,Calif.). Biopsy allograft pathological evaluation was also obtained andsamples were classified as having a particular ISHLT rejection grade(for cardiac) or acute rejection, chronic rejection, acute tubularnecrosis or no disease (for renal).

28 renal biopsy tissue samples were transferred to RLT buffer,homogenized and RNA was prepared using RNeasy preparation kits (Qiagen,Valencia, Calif.). Average total RNA yield was 1.3 ug. Samples weresubjected to on column DNAse digestion. 18 samples were derived frompatients with ongoing acute allograft rejection and 10 were fromcontrols with chronic rejection or acute renal failure.

RNA from the samples was used for amplification, labeling andhybridization to leukocyte arrays (example 11). Significance analysisfor microarrays (SAM, Tusher 2001, Example 15) was used to identifygenes that were differentially expressed between the acute rejectionsamples and controls. Leukocyte markers of acute rejection that areassociated with the graft should be genes that are expressed at somelevel in activated leukocytes. Since leukocytes appear in graft tissuewith some frequency with acute rejection, leukocyte genes associate withrejection are identified by SAM as upregulated in acute rejection inthis experiment. 35 genes were identified as upregulated in acuterejection by SAM with less than a 5% false detection rate and 139 weredetected with <10.0% FDR. Results of this analysis are shown in Table 8.

For each of these genes, to 50mer oligonucleotide sequence was used tosearch NCBI databases including Unigene and OMIM. Genes were identifiedby sequence analysis to be either known leukocyte specific markers,known leukocyte expressed markers, known not to be leukocyte expressedor expression unknown. This information helped selected candidateleukocyte markers from all upregulated genes. This is necessary becausesome of the upregulated genes may have been expressed by renal tissue.Those genes that are leukocyte specific or leukocyte expressed wereselected for evaluation by PCR in urine and blood samples from patientswith and without acute allograft rejection (cardiac and renal). Thesegenes are useful expression markers of acute rejection in allografttissue specimens and may also be useful gene expression markers for theprocess in circulating leukocytes, or urine leukocytes. Genes with knownleukocyte expression are noted in Table 8. In addition, some of theleukocyte expressed genes from this analysis were selected for PCRvalidation and development for diagnosis of acute cardiac rejection andare noted in Table 2.

Five cardiac rejection markers in the peripheral blood were assayedusing real-time PCR in renal biopsy specimens. The average fold changefor these genes between acute rejection (n=6) and controls (n=6) isgiven below. Work is ongoing to increase the number of samples testedand the significance of the results.

PCR Assays of Cardiac Rejection Peripheral Blood Markers in RenalAllograft Tissue. R=Rejection, NR=No Rejection.

Gene Fold change (R/NR) Granzyme B 2.16 CD20 1.42 NK cell receptor 1.72T-box 21 1.74 IL4 1.3

Markers of renal rejection that are secreted from cells may be measuredin the urine or serum of patients as a diagnostic or screening assay forrejection. Genes with lower molecular weight are most likely to befiltered into the urine to be measured in this way. Standardimmunoassays may be used to measure these proteins. In table 8, genesthat are known to be secreted are noted.

Example 17 Microarray and PCR Gene Expression Panels for Diagnosis andMonitoring of Acute Allograft Rejection

Array Panels/Classification Models

Using the methods of the invention, gene expression panels werediscovered for screening and diagnosis of acute allograft rejection.Gene expression panels can be implemented for diagnostic testing usingany one of a variety of technologies, including, but not limited to,microarrays and real-time PCR.

Using peripheral blood mononuclear cell RNA that was collected andprepared from cardiac allograft recipients as described in examples 2and 5, leukocyte gene expression profiles were generated and analyzedusing microarrays as described in examples 11, 13, and 15. 300 sampleswere analyzed. ISHLT rejection grades were used to divide patients intoclasses of rejection and no rejection. Multiple Additive RegressionTrees (MART, Friedman, J H 1999, example 15) was used to build a geneexpression panel and algorithm for the diagnosis of rejection with highsensitivity. Default settings for the implementation of MART calledTreeNet 1.0 (Salford Systems, San Diego, Calif.) were used except wherenoted.

82 Grade 0 (rejection) samples and 76 Grade 1B-4 (no rejection) sampleswere divided into training (80% of each class) and testing (20% of eachclass) sets. A MART algorithm was then developed on the training set todistinguish rejection from no rejection samples using a cost of 1.02:1for misclassification of rejection as no rejection. The resultingalgorithm was then used to classify the test samples. The algorithmcorrectly classified 51 of 66 (77%) no rejection samples in the trainingset and 9 of 16 (56%) no rejection samples in the test set. Forrejection samples 64 of 64 (100%) were correctly classified in thetraining set and 12 of 12 were correctly classified in the test set. Thealgorithm used 37 genes. MART ranks genes by order of importance to themodel. In order, the 37 genes were: SEQ IDs: 3058, 3030, 3034, 3069,3081, 3072, 3041, 3052, 3048, 3045, 3059, 3075, 3024, 279, 3023, 3053,3022, 3067, 3020, 3047, 3033, 3068, 3060, 3063, 3028, 3032, 3025, 3046,3065, 3080, 3039, 3055, 49, 3080, 3038, 3071.

Another MART model was built by excluding samples derived from patientsin the first month post transplant and from patients with known CMVinfection. 20 Grade 0 (rejection) samples and 25 Grade 1B-4 (norejection) samples were divided into training (80% of each class) andtesting (20% of each class) sets. A MART algorithm was then developed onthe training set to distinguish rejection from no rejection samplesusing default settings. The resulting algorithm was then used toclassify the test samples. The algorithm correctly classified 100% ofsamples of both classes in the training and testing sets. However, thismodel required 169 genes. The sample analysis was done a second timewith the only difference being requirement that all decision trees inthe algorithm be composed of two nodes (single decision, “stump model”).In this case 15/16 no rejection samples were correctly identified in thetraining set and 4/4 no rejection samples were correctly identified inthe test set. For the rejection samples, 17/19 were correctly identifiedin the training set and 5/6 were correctly classified in the test set.This model required 23 genes. In order of importance, they were: SEQIDs: 3042, 2783, 3076, 3029, 3026, 2751, 3036, 3073, 3035, 3050, 3051,3027, 3074, 3062, 3044, 3077, 2772, 3049, 3043, 3079, 3070, 3057, 3078.

Real-Time PCR Panels/Classification Models

PCR primers were developed for top rejection markers and used inreal-time PCR assays on transplant patient samples as described inexamples 12 and 13. This data was used to build PCR gene expressionpanels for diagnosis of rejection. Using MART (example 15) a 10-foldcross validated model was created to diagnose rejection using 12 norejection samples (grade 0) and 10 rejection samples (grade 3A). Defaultsettings were used with the exception of assigning a 1.02:1 cost formisclassification of rejection as no rejection and requirement that alldecision trees be limited to 2 nodes (“stump model”). 20 genes were usedin the model, including: SEQ IDs:101, 3021, 102, 2781, 78, 87, 86, 36,77, 2766, 3018, 80, 3019, 2752, 79, 99, 3016, 2790, 3020, 3056, 88. The10-fold cross-validated sensitivity for rejection was 100% and thespecificity was 85%. Some PCR primers for the genes are listed in Table2C and the sequence listing.

A different analysis of the PCR data was performed using the nearestshrunken centroids classifier (Tibshirani et al. 2002; PAM version 1.01,see example 15). A 10-fold cross validated model was created to diagnoserejection using 13 no rejection samples (grade 0) and 10 rejectionsamples (grade 3A). Default settings were used with the exception ofusing a prior probability setting of (0.5, 0.5). The algorithm derivesalgorithms using any number of the genes. A 3-gene model was highlyaccurate with a 10 fold cross-validated sensitivity for rejection of90%, and a specificity of 85%.

The 3 genes used in this model were: SEQ IDs 2784, 79, and 2794. Some ofthe PCR primers used are given in Table 2C and the sequence listing. AnROC curve was plotted for the 3-gene model and is shown in FIG. 13.

Example 18 Assay Sample Preparation

In order to show that XDx's leukocyte-specific markers can be detectedin whole blood, we collected whole blood RNA using the PAXgene wholeblood collection, stabilization, and RNA isolation kit (PreAnalytix).Varying amounts of the whole blood RNA were used in the initial RTreaction (1, 2, 4, and 8 ug), and varying dilutions of the different RTreactions were tested (1:5, 1:10, 1:20, 1:40, 1:80, 1:160). We didreal-time PCR assays with primers specific to XDx's markers and showedthat we can reliably detect these markers in whole blood.

Total RNA was prepared from 14 mononuclear samples (CPT, BD) paired with14 whole blood samples (PAXgene, PreAnalytix) from transplantrecipients. cDNA was prepared from each sample using 2 ug total RNA asstarting material. Resulting cDNA was diluted 1:10 and Sybr greenreal-time PCR assays were performed.

For real-time PCR assays, Ct values of 15-30 are desired for each gene.If a gene's Ct value is much above 30, the result may be variable andnon-linear. For PAX sample, target RNA will be more dilute than in CPTsamples. cDNA dilutions must be appropriate to bring Ct values to lessthan 30. Ct values for the first 5 genes tested in this way are shown inthe table below for both whole blood RNA (PAX) and mononuclear RNA(CPT).

Gene Ct PAX Ct CPT CD20 27.41512 26.70474 4761 28.45656 26.52635 309629.09821 27.83281 GranzymeB 31.18779 30.56954 IL4 33.11774 34.8002 Actin19.17622 18.32966 B-GUS 26.89142 26.92735

With one exception, the genes have higher Ct values in whole blood.Using this protocol, all genes can be detected with Cts<35. For genesfound to have Ct values above 30 in target samples, less diluted cDNAmay be needed.

Example 19 Allograft Rejection Diagnostic Gene Sequence Analysis

Gene products that are secreted from cells or expressed as surfaceproteins have special diagnostic utility in that an assay may bedeveloped to detect relative quantities of proteins in blood plasma orserum. Secreted proteins may also be detectable in urine, which may be auseful sample for the detection of rejection in renal allograftrecipients. Cell surface markers may be detected using antigen specificantibodies in ELISA assays or using flow sorting techniques such asFACS.

Each gene that is found to be differentially regulated in one populationof patients has several potential applications. It may be a target fornew pharmaceuticals, a diagnostic marker for a condition, a benchmarkfor titrating drug delivery and clearance, or used in screening smallmolecules for new therapeutics. Any of these applications may beimproved by an understanding of the physiologic function andlocalization of the gene product in vivo and by relating those functionsto known diseases and disorders. Identifying the basic function of eachcandidate gene helps identify the signaling or metabolic pathways thegene is a part of, leading us to investigate other members of thosepathways as potential diagnostic markers or targets of interest to drugdevelopers.

For each of the markers in table 2, we attempted to identify the basicfunction and subcellular localization of the gene. These results aresummarized in Table 9. In addition to initial DNA sequencing andprocessing, sequence analysis, and analysis of novel clones, informationwas obtained from the following public resources: Online MendelianInheritance in Man at the NCBI, LocusLink at the NCBI, the SWISS-PROTdatabase, and Protein Reviews on the Web. For each marker represented bya curated reference mRNA from the RefSeq project, the correspondingreference protein accession number is listed. Curated sequences arethose that have been manually processed by NCBI staff to represent thebest estimate of the mRNA sequence as it is transcribed, based onalignments of draft DNA sequence, predicted initiation, termination andsplice sites, and submissions of EST and full-length mRNA sequences fromthe scientific community.

These methods were used to derive the data in Table 2E.

Example 20 Detection of Proteins Expressed by Diagnostic Gene Sequences

One of ordinary skill in the art is aware of many possible methods ofprotein detection. The following example illustrates one possiblemethod.

The designated coding region of the sequence is amplified by PCR withadapter sequences at either end for subcloning. An epitope or otheraffinity “tag” such as a “His-tag” may be added to facilitatepurification and/or detection of the protein. The amplified sequence isinserted into an appropriate expression vector, most typically a shuttlevector which can replicate in either bacteria, most typically E. coli,and the organism/cell of choice for expression such as a yeast ormammalian cell. Such shuttle vectors typically contain origins ofreplication for bacteria and an antibiotic resistance marker forselection in bacteria, as well as the relevant replication and selectionsequences for transformation/transfection into the ultimate expressioncell type. In addition, the sequence of interest is inserted into thevector so that the signals necessary for transcription (a promoter) andtranslation operably linked to the coding region. Said expression couldbe accomplished in bacteria, fungi, or mammalian cells, or by in vitrotranslation.

The expression vector would then typically be used to transform bacteriaand clones analyzed to ensure that the proper sequence had been insertedinto the expression vector in the productive orientation for expression.Said verified expression vector is then transfected into a host cell andtransformants selected by a variety of methods including antibioticresistance or nutritional complementation of an auxotrophic marker. Saidtransformed cells are then grown under conditions conducive toexpression of the protein of interest, the cells and conditioned mediaharvested, and the protein of interest isolated from the most enrichedsource, either the cell pellet or media.

The protein is then be isolated by standard of chromatographic or othermethods, including immunoaffinity chromatography using the affinity“tag” sequence or other methods, including cell fractionation, ionexchange, size exclusion chromatography, or selective precipitation. Theisolated and purified protein is then be used as an antigen to generatespecific antibodies. This is accomplished by standard methods includinginjection into heterologous species with an adjuvant, isolation ofmonoclonal antibodies from mice, or in vitro selection of antibodiesfrom bacteriophage display antibody libraries. These antibodies are thenused to detect the presence of the indicated protein of interest in acomplex bodily fluid using standard methods such as ELISA or RIA.

Example 21 Detecting Changes in the Rate of Hematopoiesis

Gene expression profiling of blood cells from cardiac allograftrecipients was done using microarrays and real-time PCR as described inother examples herein.

Two of the genes in that were most correlated with cardiac transplantacute rejection with both microarrays and PCR were hemoglobin Beta and2,3 DPGM. These genes are well know to be specific markers oferythrocyte lineages. This correlation was found using both purifiedperipheral mononuclear cells and whole blood RNA preparations.

Analysis of the five genes from the PCR data most strongly correlatedwith rejection showed that their expression levels were extremely highlycorrelated within each other (R2>0.85).

Gene Hs Acc SEQ ID No hemoglobin, beta (HB B) Hs.155376 NM_000518 862,3-bisphosphoglycerate Hs.198365 X04327 87 mutase (BPC) cDNA FLJ20347Hs.102669 AK000354 94 602620663F1cDNA Hs.34549 AI123826 107 HA 1247 cDNAHs.33757 AI114652 91

This suggested that they were all elevated as part of a single responseor process. When the microarray data was used to cluster these geneswith each other and the other genes on the microarray, we found thatthese five genes clustered reasonably near each and of the other arraygenes which clustered tightly with them, four of the top 40 or so wereplatelet related genes. In addition, these a number of these genesclustered closely with CD34. CD34 is a marker of hematopoietic stemcells and is seen in the peripheral blood with increased hematopoiesis.

CD34, platelet RNA and erythrocyte RNA all mark immature or progenitorblood cells and it is clear that theses marker of acute rejection arepart of a coordinated hematopoietic response. A small increase in therate of production of RBCs and platelets may result in large foldchanges in RNA levels. Immune activation from acute rejection may leadto increased hematopoiesis in the bone marrow and non-marrow sites. Thisleads to an increase in many lineages because of the lack of completespecificity of the marrow response. Alternatively, increasedhematopoiesis may occur in a transplant recipient due to an infection(viral or other), allergy or other stimulus to the system. This resultsin production of cells or a critical mass of immune cells that can causerejection. In this scenario, monitoring for markers of immune activationwould provide an opportunity for early diagnosis.

TABLE 1 Disease Classification Disease/Patient Group CardiovascularDisease Atherosclerosis Unstable angina Myocardial Infarction Restenosisafter angioplasty Congestive Heart Failure Myocarditis EndocarditisEndothelial Dysfunction Cardiomyopathy Cardiovascular drug useInfectious Disease Hepatitis A, B, C, D, E, G Malaria Tuberculosis HIVPneumocystis Carinii Giardia Toxoplasmosis Lyme Disease Rocky MountainSpotted Fever Cytomegalovirus Epstein Barr Virus Herpes Simplex VirusClostridium Dificile Colitis Meningitis (all organisms) Pneumonia (allorganisms) Urinary Tract Infection (all organisms) Infectious Diarrhea(all organisms) Anti-infectious drug use Angiogenesis Pathologicangiogenesis Physiologic angiogenesis Treatment induced angiogenesis Proor anti-angiogenic drug use Transplant Rejection Heart Lung LiverPancreas Bowel Bone Marrow Stem Cell Graft versus host diseaseTransplant vasculopathy Skin Cornea Islet Cells Kidney XenotransplantsMechanical Organ Immunosupressive drug use Hematological DisordersAnemia - Iron Deficiency Anemia - B12, Folate deficiency Anemia -Aplastic Anemia - hemolytic Anemia - Renal failure Anemia - Chronicdisease Polycythemia rubra vera Pernicious anemia IdiophicThrrombocytopenic purpura Thrombotic Thrombocytopenic purpura Essentialthrombocytosis Leukemia Cytopenias due to immunosupression Cytopeniasdue to Chemotherapy Myelodysplasia

TABLE 2A SEQ ID SEQ ID Gene Gene Name 50mer HS ACC RNA/cDNA HSRRN18S 18Sribosomal RNA 1 NA X03205 333 ACTB Actin, beta 2 Hs.288061 NM_001101 334GUSB Glucuronidase, beta 3 Hs.183868 NM_000181 335 B2M beta 2microglobulin 4 Hs.75415 NM_004048 336 TSN Translin 5 Hs.75066 NM_004622337 CCR7 1707 6 Hs.1652 NM_001838 338 IL1R2 4685-IL1R 7 Hs.25333NM_004633 339 AIF-1 Allograft inflammatory factor 1, all 8 Hs.76364NM_004847 340 variants ALAS2 ALAS2 9 Hs.323383 NM_000032.1 341 APELINAPELIN 10 Hs.303084 NM_017413 342 CD80 B7-1, CD80 11 Hs.838 NM_005191343 EPB41 Band 4.1 12 Hs.37427 NM_004437 344 CBLB c-cbl-B 13 Hs.3144NM_004351 345 CCR5 CCR5 14 Hs.54443 NM_000579 346 MME CD10 15 Hs.1298NM_000902 347 KLRC1 CD159a 16 Hs.74082 NM_002259 348 FCGR3A CD16 17Hs.176663 NM_000569 349 FCGR3B CD16b 18 Hs.372679 NM_000570 350 LAG3CD223 19 Hs.74011 NM_002286 351 PECAM1 CD31 20 Hs.78146 NM_000442 352CD34 CD34 21 Hs.374990 NM_001773 353 FCGR1A CD64 22 Hs.77424 NM_000566354 TFRC CD71 = T9, transferrin receptor 23 Hs.77356 NM_003234 355 CMA1chymase 24 Hs.135626 NM_001836 356 KIT c-Kit 25 Hs.81665 NM_000222 357MPL c-mpl 26 Hs.84171 NM_005373 358 EphB6 EphB6 27 Hs.3796 NM_004445 359EPOR EPO-R 28 Hs.127826 NM_000121.2 360 Foxp3 Foxp3 29 Hs.247700NM_014009 361 GATA1 GATA1 30 Hs.765 NM_002049 362 ITGA2B GP IIb 31NM_000419.2 NM_000419 363 GNLY granulysin 32 Hs.105806 NM_006433 364GZMA GZMA 33 Hs.90708 NM_006144 365 HBA hemoglobin, alpha 1 34 Hs.398636NM_000558.3 366 HBZ hemoglobin, zeta 35 Hs.272003 NM_005332.2 367 HBBhemoglobin, beta 36 Hs.155376 NM_000518.4 368 HBD hemoglobin, delta 37Hs.36977 NM_000519.2 369 HBE hemoglobin, epsilon 1 38 Hs.117848NM_005330 370 HBG hemoglobin, gamma A 39 Hs.283108 NM_000559.2 371 HBQhemoglobin, theta 1 40 Hs.247921 NM_005331 372 HLA-DP MH/c, class II, DPalpha 1 41 Hs.198253 NM_033554 373 HLA-DQ MHC, class II, DQ alpha 1 42Hs.198253 NM_002122 374 HLA-DRB MHC, class II, DR beta 1 43 Hs.375570NM_002124.1 375 ICOS ICOS 44 Hs.56247 NM_012092 376 IL18 IL18 45Hs.83077 NM_001562 377 IL3 interleukin 3 (colony-stimulating 46 Hs.694NM_000588 378 factor, multiple) ITGA4 Integrin, alpha 4 (antigen CD49D,47 Hs.40034 NM_000885 379 alpha 4 subunit of VLA-4 receptor) ITGAMintegrin, alpha M (complement 48 Hs.172631 NM_000632 380 componentreceptor 3, alpha; also known as CD11b (p170), macrophage antigen alphapolypeptide) ITGB7 integrin, beta 7 49 Hs.1741 NM_000889 381 CEBPB LAP,CCAAT/enhancer binding 50 Hs.99029 NM_005194 382 protein (C/EBP), betaNF-E2 NF-E2 51 Hs.75643 NM_006163 383 PDCD1 programmed cell death 1,PD-1 52 Hs.158297 NM_005018 384 PF4 platelet factor 4 (chemokine (C-X-C53 Hs.81564 NM_002619 385 motif) ligand 4) PRKCQ protein kinase C, theta54 Hs.211593 NM_006257.1 386 PPARGC1 PPARgamma 55 Hs.198468 NM_013261387 RAG1 recombination activating gene 1 56 Hs.73958 NM_000448 388 RAG2recombination activating gene 2 57 Na NM_000536 389 CXCL12 chemokine(C-X-C motif) ligand 12 58 Hs.237356 NM_000609 390 (stromal cell-derivedfactor 1) (SDF-1) TNFRSF4 tumor necrosis factor receptor 59 Hs.129780NM_003327 391 superfamily, member 4 TNFSF4 tumor necrosis factor(ligand) 60 Hs.181097 NM_003326 392 superfamily, member 4 (tax-transcriptionally activated glycoprotein 1, 34kDa) TPS1 tryptase, alpha61 Hs.334455 NM_003293 393 ADA ADA adenosine deaminase 62 Hs.1217NM_000022 394 CPM Carboxypeptidase M 63 Hs.334873 NM_001874.1 395 CSF2colony stimulating factor, GM-CSF 64 Hs.1349 NM_000758.2 396 CSF3 colonystimulating factor 3, G-CSF 65 Hs.2233 NM_172219 397 CRP C-reactiveprotein, pentraxin-related 66 Hs.76452 NM_000567.1 398 (CRP), FLT3FMS-Related Tyrosine Kinase 3 67 Hs.385 NM_004119 399 GATA3 GATA bindingprotein 3 68 Hs.169946 NM_002051.1 400 IL7R Interleukin 7 receptor 69Hs.362807 NM_002185.1 401 KLF1 Kruppel-like factor 1 (erythroid), 70Hs.37860 NM_006563.1 402 EKLF LCK lymphocyte-specific protein tyrosine71 Hs.1765 NM_005356.2 403 kinase LEF1 lymphoid enhancer-binding factor1 72 Hs.44865 NM_016269.2 404 PLAUR Urokinase-type Plasminogen 73Hs.179657 NM_002659.1 405 Activator Receptor, CD87, uPAR TNFSF13B Tumornecrosis factor (ligand) 74 Hs.270737 NM_006573.3 406 superfamily,member 13b, BlyS/TALL-1/BAFF IL8 Interleukin 8 75 Hs.624 NM_000584 407GZMB Granzyme B (granzyme 2, cytotoxic 76 Hs.1051 NM_004131 408T-lymphocyte-associated serine esterase 1) TNFSF6 Tumor necrosis factor(ligand) 77 Hs.2007 NM_000639 409 superfamily, member 6 TCIRG1 T-cell,immune regulator 1, ATPase, 78 Hs.46465 NM_006019 410 H+ transporting,lysosomal V0 protein a isoform 3 PRF1 Perforin 1 (pore forming protein)79 Hs.2200 NM_005041 411 IL4 Interleukin 4 80 Hs.73917 NM_000589 412IL13 Interleukin 13 81 Hs.845 NM_002188 413 CTLA4 CytotoxicT-lymphocyte-associated 82 Hs.247824 NM_005214 414 protein 4 CD8A CD8antigen, alpha polypeptide (p32) 83 Hs.85258 NM_001768 415 BY55 Naturalkiller cell receptor, 84 Hs.81743 NM_007053 416 immunoglobulinsuperfamily member OID_4460 EST 85 Hs.205159 AF150295 417 HBBHemoglobin, beta 86 Hs.155376 NM_000518 418 BPGM 2,3-bisphosphoglyceratemutase 87 Hs.198365 NM_001724 419 MTHFD2 Methylene tetrahydrofolate 88Hs.154672 NM_006636 420 dehydrogenase (NAD+ dependent),methenyltetrahydrofolate cyclohydrolase TAP1 Transporter 1, ATP-bindingcassette, 89 Hs.352018 NM_000593 421 sub-family B (MDR1/TAP) KPNA6Karyopherin alpha 6 (importin alpha 90 Hs.301553 AW021037 422 7)OID_4365 Mitochondrial solute carrier 91 Hs.300496 AI114652 423 IGHMImmunoglobulin heavy constant mu 92 Hs.300697 BC032249 424 OID_573KIAA1486 protein 93 Hs.210958 AB040919 425 OID_873 KIAA1892 protein 94Hs.102669 AK000354 426 OID_3 EST 95 Hs.104157 AW968823 427 CXCR4Chemokine (C-X-C motif) receptor 4 96 Hs.89414 NM_003467 428 CD69 CD69antigen (p60, early T-cell 97 Hs.82401 NM_001781 429 activation antigen)CCL5 Chemokine (C-C motif) ligand 5 98 Hs.241392 NM_002985 430 (RANTES,SCYA5) IL6 Interleukin 6 99 Hs.93913 NM_000600 431 IL2 Interleukin 2 100Hs.89679 NM_000586 432 KLRF1 Killer cell lectin-like receptor 101Hs.183125 NM_016523 433 subfamily F, member 1 LYN v-yes-1 Yamaguchisarcoma viral 102 Hs.80887 NM_002350 434 related oncogene homolog IL2RAInterleukin 2 receptor, alpha 103 Hs.1724 NM_000417 435 CCL4 Chemokine(C-C motif) ligand 4, 104 Hs.75703 NM_002984 436 SCYA4 OID_6207 EST 105Hs.92440 D20522 437 ChGn Chondroitin beta 1,4 N- 106 Hs.11260 NM_018371438 acetylgalactosaminyltransferase OID_4281 EST 107 Hs.34549 AA053887439 CXCL9 Chemokine (C-X-C motif) ligand 9 108 Hs.77367 NM_002416 440(MIG) CXCL10 Chemokine (C-X-C motif) ligand 10, 109 Hs.2248 NM_001565441 SCYB10 IL17 Interleukin 17 (cytotoxic T- 110 Hs.41724 NM_002190 442lymphocyte-associated serine esterase 8) IL15 Interleukin 15 111Hs.168132 NM_000585 443 IL10 Interleukin 10 112 Hs.193717 NM_000572 444IFNG Interferon, gamma 113 Hs.856 NM_000619 445 HLA-DRB1 Majorhistocompatibility complex, 114 Hs.308026 NM_002124 446 class 11, DRbeta 1 CD8B1 CD8 antigen, beta polypeptide 1 115 Hs.2299 NM_004931 447(p37) CD4 CD4 antigen (p55) 116 Hs.17483 NM_000616 448 CXCR3 Chemokine(C-X-C motif) receptor 3, 117 Hs.198252 NM_001504 449 GPR9 OID_7094 XDxEST 479G12 118 NA NA 450 OID_7605 EST 119 Hs.109302 AA808018 451 CXCL1Chemokine (C-X-C motif) ligand 1 120 Hs.789 NM_001511 452 (melanomagrowth stimulating activity, alpha) OID_253 EST 121 Hs.83086 AK091125453 GPI Glucose phosphate isomerase 122 Hs.409162 NM_000175 454 CD47CD47 antigen (Rh-related antigen, 123 Hs.82685 NM_001777 455integrin-associated signal transducer) HLA-F Major histocompatibilitycomplex, 124 Hs.377850 NM_018950 456 class I, F OID_5350 EST 125 Hs.4283AK055687 457 TCRGC2 T cell receptor gamma constant 2 126 Hs.112259M17323 458 OID_7016 EST 127 NA B1018696 459 PTGS2Prostaglandin-endoperoxide synthase 128 Hs.196384 NM_000963 460 2(prostaglandin G/H synthase and cyclooxygenase) OID_5847 Hypotheticalprotein FLJ32919 129 Hs.293224 NM_144588 461 PRDM1 PR domain containing1,with ZNF 130 Hs.388346 NM_001198 462 domain CKB Creatine kinase, Brain131 Hs.173724 NM_001823 463 TNNI3 Troponin I, cardiac 132 Hs.351382NM_000363 464 TNNT2 Troponin T2, cardiac 133 Hs.296865 NM_000364 465 MBMyoglobin 134 Hs.118836 NM_005368 466 SLC7A11 Solute carrier family 7,(cationic 135 Hs.6682 NM_014331 467 amino acid transporter, y+ system)member 11 TNFRSF5 tumor necrosis factor receptor 136 Hs.25648 NM_001250468 superfamily, member 5; CD40 TNFRSF7 tumor necrosis factor receptor137 Hs.355307 NM_001242 469 superfamily, member 7; CD27 CD86 CD86antigen (CD28 antigen ligand 138 Hs.27954 NM_175862 470 2, B7-2 antigen)AIF1v2 Allograft inflammatory factor 1, 139 Hs.76364 NM_004847 471splice valiant 2 EBV BCLF-1 BCLF-1 major capsid 140 NA AJ507799 472 EBVEBV EBNA repetitive sequence 141 NA AJ507799 473 CMV p67 pp67 142 NAX17403 474 CMV TRL7 c6843-6595 143 NA X17403 475 CMV IE1e3 IE1 exon3 144NA X17403 476 CMV IE1e4 IE1 exon 4 (40 variants) 145 NA X17403 477 EBVEBNA-1 EBNA-1 coding region 146 NA AJ507799 478 EBV BZLF-1 Zebra gene147 NA AJ507799 479 EBV EBN EBNA repetitive sequence 148 NA AJ507799 480EBV EBNA-LP Short EBNA leader peptide exon 149 NA AJ507799 481 CMV IE1IE1S 150 NA X17403 482 CMV IE1 IE1-MC (exon3) 151 NA X17403 483 CLCCharot-Leyden crystal protein 152 Hs.889 NM_001828 484 TERF2IP telomericrepeat binding factor 2, 153 Hs.274428 NM_018975 485 interacting proteinHLA-A Major histocompatibility complex, 154 Hs.181244 NM_002116 486class I, A OID_5891 EST 3′ end 155 None AW297949 487 MSCP mitochondrialsolute carrier protein 156 Hs.283716 NM_018579 488 DUSP5 dualspecificity phosphatase 5 157 Hs.2128 NM_004419 489 PRO1853 Hypotheticalprotein PRO1853 158 Hs.433466 NM_018607 490 OID_6420 73A7, FLJ00290protein 159 Hs.98531 AK090404 491 CDSN Corneodesmosin 160 Hs.507NM_001264 492 OID_4269 EST 161 Hs.44628 BM727677 493 RPS25 Ribosomalprotein S25 162 Hs.409158 NM_001028 494 GAPD Glyceraldehyde-3-phosphate163 Hs.169476 NM_002046 495 dehydrogenase RPLP1 Ribosomal protein,large, P1 164 Hs.424299 NM_001003 496 OID_5115 qz23b07.x1 cDNA, 3′ end165 NA AI364926 497 /clone = IMAGE:2027701 SLC9A8 Solute carrier family9 166 Hs.380978 AB023156 498 (sodium/hydrogen exchanger), isoform 8OID_1512 IMAGE:3865861 5 clone 5′ 167 Hs.381302 BE618004 499 POLR2DPolymerase (RNA) II (DNA directed) 168 Hs.194638 NM_004805 500polypeptide D ARPC3 Actin related protein ⅔ complex, 169 Hs.293750NM_005719 501 subunit 3, 21kDa OID_6282 EST 3′ end 170 Hs.17132 BC041913502 PRO1073 PRO1073 protein 171 Hs.356442 AF001542 503 OID_7222 EST,weakly similar to A43932 172 Hs.28310 BG260891 504 mucin 2 precursor,intestinal FPRL1 Formyl peptide receptor-like 1 173 Hs.99855 NM_001462505 FKBPL FK506 binding protein like 174 Hs.99134 NM_022110 506 PREBProlactin regulatory element binding 175 Hs.279784 NM_013388 507OID_1551 Hypothetical protein LOC200227 176 Hs.250824 BE887646 508OID_7595 DKFZP566F0546 protein 177 Hs.144505 NM_015653 509 RNF19 Ringfinger protein 19 178 Hs.48320 NM_015435 510 SMCY SMC (mouse) homolog, Y179 Hs.80358 NM_004653 511 chromosome (SMCY) OID_4184 CMV HCMVUL109 180NA X17403 512 OID_7504 Hypothetical protein FLJ35207 181 Hs.86543NM_152312 513 DNAJC3 DnaJ (Hsp40) homolog, subfamily C, 182 Hs.9683NM_006260 514 member 3 ARHU Ras homolog gene family, member U 183Hs.20252 NM_021205 515 OID_7200 Hypothetical protein FLJ22059 184Hs.13323 NM_022752 516 SERPINB2 Serine (or cysteine) protemase 185Hs.75716 NM_002575 517 inhibitor, clade B (ovalbumin), member 2 ENO1Enolase 1, alpha 186 Hs.254105 NM_001428 518 OID_7696 EST 3′ end 187Hs.438092 AW297325 519 OID_4173 CMV HCMVTRL2 (IRL2) 188 NA X17403 520CSF2RB Upstream variant mRNA of colony 189 Hs.285401 AL540399 521stimulating factor 2 receptor, beta, low-affinity (granulocyte-macrophage) OID_7410 CM2-LT0042-281299-062-e11 190 Hs.375145 AW837717522 LT0042 cDNA, mRNA sequence OID_4180 CMV HCMVUS28 191 NA X17403 523OID_5101 EST 192 Hs.144814 BG461987 524 MOP3 MOP-3 193 Hs.380419NM_018183 525 RPL18A Ribosomal protein L18a 194 Hs.337766 NM_000980 526INPP5A Inositol polyphosphate-5- 195 Hs.124029 NM_005539 527phosphatase, 40kDa hIAN7 Immune associated nucleotide 196 Hs.124675BG772661 528 RPS29 Ribosomal protein S29 197 Hs.539 NM_001032 529OID_6008 EST 3′ end 198 Hs.352323 AW592876 530 OID_4186 CMV HCMVUL122199 NA X17403 531 VNN2 vanin 2 200 Hs.121102 NM_004665 532 OID_7703KIAA0907 protein 201 Hs.24656 NM_014949 533 OID_7057 480F8 202 NA 480F8534 OID_4291 EST 203 Hs.355841 BC038439 535 OID_1366 EST 204 Hs.165695AW850041 536 EEF1A1 Eukaryotic translation elongation 205 Hs.422118NM_001402 537 factor 1 alpha 1 PA2G4 Proliferation-associated 2G4, 38kDa206 Hs.374491 NM_006191 538 GAPD Glyceraldehyde-3-phosphate 207Hs.169476 NM_002046 539 dehydrogenase CHD4 Chromodomain helicase DNA 208Hs.74441 NM_001273 540 binding protein 4 OID_7951 E2F-like protein(LOC51270) 209 Hs.142908 NM_016521 541 DAB1 Disabled homolog 1(Drosophila) 210 Hs.344127 NM_021080 542 OID_3406 Hypothetical proteinFLJ20356 211 Hs.61053 NM_018986 543 OID_6986 462H9 EST 212 Hs.434526AK093608 544 OID_5962 EST 3′ end 213 Hs.372917 AW452467 545 OID_5152 EST3′ end 214 Hs.368921 AI392805 546 S100A8 S100 calcium-binding protein A8215 Hs.416073 NM_002964 547 calgranulin A) HNRPU HNRPU Heterogeneousnuclear 216 Hs.103804 BM467823 548 ribonucleoprotein U (scaffoldattachment factor A) ERCC5 Excision repair cross-complementing 217Hs.48576 NM_000123 549 rodent repair deficiency, complementation group 5(xeroderma pigmentosum, complementation group G (Cockayne syndrome))RPS27 Ribosomal protein S27 218 Hs.195453 NM_001030 550(metallopanstimulin 1) ACRC acidic repeat containing (ACRC), 219Hs.135167 NM_052957 551 PSMD11 Proteasome (prosome, macropain) 220Hs.90744 AI684022 552 26S subunit, non-ATPase, 11 OID_1016 FLJ00048protein 221 Hs.289034 AK024456 553 OID_1309 AV706481 cDNA 222 NoneAV706481 554 OID_7582 Weakly similar to ZINC FINGER 223 Hs.16493AK027866 555 PROTEIN 142 OID_4317 ta73c09.xl 3′ end 224 Hs.387179AI318342 556 /clone = IMAGE:2049712 Ribosomal Protein S15 OID_5889 3′end/clone = IMAGE:3083913 225 Hs.255698 AW297843 557 UBL1 Ubiquitin-like1 (sentrin) 226 Hs.81424 NM_003352 558 OID_3687 EST 227 None W03955 559OID_7371 EST 5′ 228 Hs.290874 BE730505 560 SH3BGRL3 SH3 domain bindingglutamic acid- 229 Hs.109051 NM_031286 561 rich protein like 3 SEMA7ASema domain, immunoglobulin 230 Hs.24640 NM_003612 562 domain (Ig), andGPI membrane anchor, (semaphorin) 7A OID_5708 EST 3′ end 231 Hs.246494AW081540 563 OID_5992 EST 3′ end 232 Hs.257709 AW467992 564 IL21Interleukin 21 233 Hs.302014 NM_021803 565 HERC3 Hect domain and RLD 3(HERC3) 234 Hs.35804 NM_014606 566 OID_7799 AluJo/FLAM SINE/Alu 235AW837717 567 P11 26 serine protease 236 Hs.997 NM_006025 568 OID_7766EST 3′ end 237 Hs.437931 AW294711 569 TIMM10 translocase of innermitochondrial 238 Hs.235750 NM_012456 570 membrane 10 (yeast) homolog(TIMM10) EGLN1 Egl nine homolog 1 (C. elegans) 239 Hs.6523 AJ310543 571TBCC Tubulin-specific chaperone c 240 Hs.75064 NM_003192 572 RNF3 Ringfinger protein 3 241 Hs.8834 NM_006315 573 OID_6451 170F9, hypotheticalprotein 242 Hs.288872 AL834168 574 FLJ21439 CCNDBP1 cyclin D-typebinding-protein 1 243 Hs.36794 NM_012142 575 (CCNDBP1) OID_8063 MUC18gene exons 1 & 2 244 NA X68264 576 SUV39H1 Suppressor of variegation 3-9245 Hs.37936 NM_003173 577 homolog 1 (Drosophila) HSPC048 HSPC048protein 246 Hs.278944 NM_014148 578 OID_5625 EST 3′ end from T cells 247Hs.279121 AW063780 579 WARS Tryptophanyl-tRNA synthetase 248 Hs.82030NM_004184 580 OID_6823 107H8 249 Hs.169610 AL832642 581 OID_7073 119F12250 Hs.13264 AL705961 582 OID_5339 EST 3′ end 251 Hs.436022 AI625119 583OID_4263 fetal retina 937202 cDNA clone 252 Hs.70877 AA136584 584IMAGE:565899 MGC26766 Hypothetical protein MGC26766 253 Hs.288156AK025472 585 SERPINB11 Serine (or cysteine) proteinase 254 Hs.350958NM_080475 586 inhibitor, clade B (ovalbumin), member 11 OID_6711 58G4,IMAGE:4359351 5′ 255 none BF968628 587 RNF10 Ring finger protein 10 256Hs.5094 NM_014868 588 MKRN1 Makorin, ring finger protein, 1 257 Hs.7838NM_013446 589 RPS16 ribosomal protein S16 258 Hs.397609 NM_001020 590BAZ1A Bromodomain adjacent to zinc finger 259 Hs.8858 NM_013448 591domain, 1A OID_5998 EST 3′ end 260 Hs.330268 AW468459 592 ATP5L ATPsynthase, H+ transporting, 261 Hs.107476 NM_006476 593 mitochondrial F0complex, subunit g OID_6393 52B9 262 NA 52B9 594 RoXaN Ubiquitoustetratricopeptide 263 Hs.25347 BC004857 595 containing protein RoXaNNCBP2 Nuclear cap binding protein subunit 264 Hs.240770 NM_007362 596 2,20kDa OID_6273 EST 3′ end 265 Hs.158976 AW294774 597 HZF12 zinc fingerprotein 12 266 Hs.164284 NM_033204 598 CCL3 Chemokine (C-C motif) ligand3 267 Hs.73817 D90144 599 OID_4323 IMAGE:1283731 3′ 268 Hs.370770AA744774 600 OID_5181 tg93h12.x1 NCI_CGAP_CLL1 269 NA AI400725 601 cDNAclone IMAGE:2116391 3′ similar to contains TAR1.t1 MER22 PRDX4Peroxiredoxin 4 270 Hs.83383 NM_006406 602 BTK Bruton agammaglobulinemiatyrosine 271 Hs.159494 NM_000061 603 kinase OID_6298 Importin betasubunit mRNA 272 Hs.180446 AI948513 604 PGK1 Phosphoglycerate kinase 1273 Hs.78771 NM_000291 605 TNFRSF10A Tumor necrosis factor receptor 274Hs.249190 NM_003844 606 superfamily, member 10a ADM adrenomedullin 275Hs.394 NM_001124 607 OID_357 138G5 276 NA 138G5 608 C20orf6 461A4chromosome 20 open reading 277 Hs.88820 NM_016649 609 frame 6 OID_3226DKFZP564O0823 protein 278 Hs.105460 NM_015393 610 ASAH1N-acylsphingosine amidohydrolase 279 Hs.75811 NM_004315 611 (acidceramidase) 1 ATF5 Activating transcription factor 5 280 Hs.9754NM_012068 612 OID_4887 hypothetical protein MGC14376 281 Hs.417157NM_032895 613 OID_4239 EST 282 Hs.177376 BQ022840 614 MDM2 Mouse doubleminute 2, homolog of; 283 Hs.170027 NM_002392 615 p53-binding protein(MDM2), transcript variant MDM2. XRN2 5′-3′ exoribonuclease 2 284Hs.268555 AF064257 616 OID_6039 Endothelial differentiation, 285Hs.122575 BE502246 617 lysophosphatidic acid G-protein- coupledreceptor, 4 (EDG4) OID_4210 IMAGE:4540096 286 Hs.374836 AI300700 618OID_7698 EST 3′ end 287 Hs.118899 AA243283 619 PRKRA Protein kinase,interferon-inducible 288 Hs.18571 NM_003690 620 double stranded RNAdependent activator OID_4288 IMAGE:2091815 289 Hs.309108 AI378046 621OID_5620 EST 3′ end from T cells 290 Hs.279116 AW063678 622 OID_7384 EST5′ 291 Hs.445429 BF475239 623 OID_1209 EST Weakly similar tohypothetical 292 Hs.439346 C14379 624 protein FLJ20378 CDKN1BCyclin-dependent kinase inhibitor 1B 293 Hs.238990 NM_004064 625 (p27,Kip1) PLOD Procollagen-lysine, 2-oxoglutarate 5- 294 Hs.75093 NM_000302626 dioxygenase (lysine hydroxylase, Ehlers-Danlos syndrome type VI)OID_5128 EST 295 Hs.283438 AK097845 627 OID_5877 EST 3′ end 296Hs.438118 AW297664 628 FZD4 Frizzled (Drosophila) homolog 4 297 Hs.19545NM_012193 629 HLA-B Major histocompatibility complex, 298 Hs.77961NM_005514 630 class I, B OID_5624 EST 3′ end from T cells 299 Hs.279120AW063921 631 FPR1 Formyl peptide receptor 1 300 Hs.753 NM_002029 632ODF2 Outer dense fiber of sperm tails 2 301 Hs.129055 NM_153437 633OID_5150 tg04g01.x1 cDNA, 3′ end 302 Hs.160981 AI392793 634 /clone =IMAGE:2107824 OID_5639 EST 3′ end from T cells 303 Hs.279139 AW064243635 OID_6619 469A10 304 NA 469A10 636 OID_6933 463C7, 4 EST hits.Aligned 305 Hs.86650 AI089520 637 OID_7049 480E2 306 NA 480E2 638 IL17CInterleukin 17C 307 Hs.278911 NM_013278 639 OID_5866 EST 3′ end 308Hs.255649 BM684739 640 CD44 CD44 309 Hs.169610 AA916990 641 VPS45AVacuolar protein sorting 45A (yeast) 310 Hs.6650 NM_007259 642 OID_4932aa92c03.r1 Stratagene fetal retina 311 NA AA457757 643 937202 cDNA cloneIMAGE:838756 OID_7821 EST 312 NA AA743221 644 OID_4916 zr76a03.r1Soares_NhHMPu_S1 313 NA AA252909 645 cDNA clone IMAGE:669292 OID_4891Hypothetical protein LOC255488 314 Hs.294092 AL832329 646 HADHBHydroxyacyl-Coenzyme A 315 Hs.146812 NM_000183 647dehydrogenase/3-ketoacyl-Coenzyme A thiolase/enoyl-Coenzyme A hydratase(trifunctional protein), beta subunit FLJ22757 Hypothetical proteinFLJ22757 316 Hs.236449 NM_024898 648 RAC1 Ras-related C3 botulinum toxin317 Hs.173737 AK054993 649 substrate 1 (rho family, small GTP bindingprotein Rac1) OID_6415 72D4, FLJ00290 protein 318 Hs.98531 CA407201 650NMES1 Normal mucosa of esophagus specific 1 319 Hs.112242 NM_032413 651DMBT1 Deleted in malignant brain tumors 1, 320 Hs.279611 NM_007329 652transcript variant 2 RPS23 ribosomal protein S23 321 Hs.3463 NM_001025653 ZF HCF-binding transcription factor 322 Hs.29417 NM_021212 654Zhangfei NFE2L3 Nuclear factor (erythroid-derived 2)- 323 Hs.22900NM_004289 655 like 3 RAD9 RAD9 homolog (S. pombe) 324 Hs.240457NM_004584 656 OID_6295 EST 3′ end 325 Hs.389327 AI880607 657 DEFCAPDeath effector filament-forming Ced- 326 Hs.104305 NM_014922 658 4-likeapoptosis protein, transcript variant B RPL27A Ribosomal protein L27a327 Hs.76064 BF214146 659 IL22 Interleukin 22 (IL22) 328 Hs.287369NM_020525 660 PSMA4 Proteasome (prosome, macropain) 329 Hs.251531NM_002789 661 subunit, alpha type, 4, (PSMA4) CCNI cyclin I (CCNI) 330Hs.79933 NM_006835 662 THBD Thrombomodulin 331 Hs.2030 NM_000361 663CGR19 Cell growth regulatory with ring 332 Hs.59106 NM_006568 664 fingerdomain Non- Median SEQ ID SEQ ID Para Rank Down 50mer Gene Gene Name ACCRNA/cDNA Score in NR Regulated 152 CLC Charot-Leyden crystal proteinNM_001828 484 779 4342 153 TERF2IP telomeric repeat binding factor 2,NM_018975 485 744 1775 interacting protein 154 HLA-A Majorhistocompatibility complex, NM_002116 486 735 125 1 class I, A 155OID_5891 EST 3′ end AW297949 487 730 7044.5 1 156 MSCP mitochondrialsolute carrier protein NM_018579 488 730 3465.5 157 DUSP5 dualspecificity phosphatase 5 NM_004419 489 726 3122.5 158 PRO1853Hypothetical protein PRO1853 NM_018607 490 725 4153 159 OID_6420 73A7,FLJ00290 protein AK090404 491 725 7000.5 160 CDSN CorneodesmosinNM_001264 492 722 2732 161 OID_4269 EST BM727677 493 715 5598.5 162RPS25 Ribosomal protein S25 NM_001028 494 710 164.5 163 GAPDGlyceraldehyde-3-phosphate NM_002046 495 707 215.5 dehydrogenase 164RPLP1 Ribosomal protein, large, P1 NM_001003 496 703 157 165 OID_5115qz23b07.x1 cDNA, 3′ end AI364926 497 703 6629 1 /clone = IMAGE:2027701166 SLC9A8 Solute carrier family 9 AB023156 498 702 2538.5(sodium/hydrogen exchanger), isoform 8 167 OID_1512 IMAGE:3865861 5clone 5′ BE618004 499 700 4008 1 168 POLR2D Polymerase (RNA) II (DNAdirected) NM_004805 500 700 4190.5 polypeptide D 169 ARPC3 Actin relatedprotein ⅔ complex, NM_005719 501 698 470.5 subunit 3, 21kDa 170 OID_6282EST 3′ end BC041913 502 697 4371.5 171 PRO1073 PRO1073 protein AF001542503 697 6754 172 OID_7222 EST, weakly similar to A43932 BG260891 504 6956759 mucin 2 precursor, intestinal 173 FPRL1 Formyl peptidereceptor-like 1 NM_001462 505 692 4084.5 174 FKBPL FK506 binding proteinlike NM_022110 506 691 1780.5 175 PREB Prolactin regulatory elementbinding NM_013388 507 690 3568 176 OID_1551 Hypothetical proteinLOC200227 BE887646 508 689 6423 1 177 OID_7595 DKFZP566F0546 proteinNM_015653 509 689 3882.5 178 RNF19 Ring finger protein 19 NM_015435 510689 7700.5 179 SMCY SMC (mouse) homolog, Y NM_004653 511 687 6074.5chromosome (SMCY) 180 OID_4184 CMV HCMVUL109 X17403 512 687 6810.5 181OID_7504 Hypothetical protein FLJ35207 NM_152312 513 686 6939 182 DNAJC3DnaJ (Hsp40) homolog, subfamily C, NM_006260 514 686 3932.5 member 3 183ARHU Ras homolog gene family, member U NM_021205 515 686 7584 184OID_7200 Hypothetical protein FLJ22059 NM_022752 516 685 2804.5 185SERPINB2 Serine (or cysteine) protemase NM_002575 517 684 4690.5inhibitor, clade B (ovalbumin), member 2 186 ENO1 Enolase 1, alphaNM_001428 518 684 327 187 OID_7696 EST 3′ end AW297325 519 683 4875.5188 OID_4173 CMV HCMVTRL2 (IRL2) X17403 520 683 4010.5 189 CSF2RBUpstream variant mRNA of colony AL540399 521 683 3753 stimulating factor2 receptor, beta, low-affinity (granulocyte- macrophage) 190 OID_7410CM2-LT0042-281299-062-e11 AW837717 522 682 7445 LT0042 cDNA, mRNAsequence 191 OID_4180 CMV HCMVUS28 X17403 523 681 4359 192 OID_5101 ESTBG461987 524 681 7272 193 MOP3 MOP-3 NM_018183 525 681 4085.5 1 194RPL18A Ribosomal protein L18a NM_000980 526 680 238 195 INPP5A Inositolpolyphosphate-5- NM_005539 527 680 4838.5 1 phosphatase, 40kDa 196 hIAN7Immune associated nucleotide BG772661 528 680 4718 197 RPS29 Ribosomalprotein S29 NM_001032 529 680 107.5 198 OID_6008 EST 3′ end AW592876 530679 6560.5 199 OID_4186 CMV HCMVUL122 X17403 531 677 4788.5 200 VNN2vanin 2 NM_004665 532 677 2620.5 201 OID_7703 KIAA0907 protein NM_014949533 676 6104.5 202 OID_7057 480F8 480F8 534 675 6862 203 OID_4291 ESTBC038439 535 674 5618.5 204 OID_1366 EST AW850041 536 674 5590.5 1 205EEF1A1 Eukaryotic translation elongation NM_001402 537 672 232 factor 1alpha 1 206 PA2G4 Proliferation-associated 2G4, 38kDa NM_006191 538 6724402 207 GAPD Glyceraldehyde-3-phosphate NM_002046 539 671 194.5dehydrogenase 208 CHD4 Chromodomain helicase DNA NM_001273 540 6712578.5 binding protein 4 209 OID_7951 E2F-like protein (LOC51270)NM_016521 541 671 4467 210 DAB1 Disabled homolog 1 (Drosophila)NM_021080 542 670 6357.5 211 OID_3406 Hypothetical protein FLJ20356NM_018986 543 669 2087 212 OID_6986 462H9 EST AK093608 544 669 4454 1213 OID_5962 EST 3′ end AW452467 545 668 5870.5 1 214 OID_5152 EST 3′end AI392805 546 668 6354.5 215 S100A8 S100 calcium-binding protein A8NM_002964 547 668 134 calgranulin A) 216 HNRPU HNRPU Heterogeneousnuclear BM467823 548 668 4108 ribonucleoprotein U (scaffold attachmentfactor A) 217 ERCC5 Excision repair cross-complementing NM_000123 549668 6430.5 rodent repair deficiency, complementation group 5 (xerodermapigmentosum, complementation group G (Cockayne syndrome)) 218 RPS27Ribosomal protein S27 NM_001030 550 668 160 (metallopanstimulin 1) 219ACRC acidic repeat containing (ACRC), NM_052957 551 668 4871.5 1 220PSMD11 Proteasome (prosome, macropain) AI684022 552 668 4138 26Ssubunit, non-ATPase, 11 221 OID_1016 FLJ00048 protein AK024456 553 6675199 222 OID_1309 AV706481 cDNA AV706481 554 667 7279.5 223 OID_7582Weakly similar to ZINC FINGER AK027866 555 667 5003.5 1 PROTEIN 142 224OID_4317 ta73c09.xl 3′ end AI318342 556 667 6499 /clone = IMAGE:2049712Ribosomal Protein S15 225 OID_5889 3′ end/clone = IMAGE:3083913 AW297843557 666 6837 1 226 UBL1 Ubiquitin-like 1 (sentrin) NM_003352 558 6661978.5 227 OID_3687 EST W03955 559 666 5519.5 228 OID_7371 EST 5′BE730505 560 665 7751.5 229 SH3BGRL3 SH3 domain binding glutamic acid-NM_031286 561 665 310 rich protein like 3 230 SEMA7A Sema domain,immunoglobulin NM_003612 562 665 3505.5 domain (Ig), and GPI membraneanchor, (semaphorin) 7A 231 OID_5708 EST 3′ end AW081540 563 665 6224.5232 OID_5992 EST 3′ end AW467992 564 665 5648 233 IL21 Interleukin 21NM_021803 565 664 5036.5 234 HERC3 Hect domain and RLD 3 (HERC3)NM_014606 566 664 3056.5 1 235 OID_7799 AluJo/FLAM SINE/Alu AW837717 567664 3544 236 P11 26 serine protease NM_006025 568 664 7173 237 OID_7766EST 3′ end AW294711 569 663 7270.5 238 TIMM10 translocase of innermitochondrial NM_012456 570 663 4779.5 membrane 10 (yeast) homolog(TIMM10) 239 EGLN1 Egl nine homolog 1 (C. elegans) AJ310543 571 6627172.5 240 TBCC Tubulin-specific chaperone c NM_003192 572 662 3384 241RNF3 Ring finger protein 3 NM_006315 573 661 4062 242 OID_6451 170F9,hypothetical protein AL834168 574 661 7126 1 FLJ21439 243 CCNDBP1 cyclinD-type binding-protein 1 NM_012142 575 661 1919 (CCNDBP1) 244 OID_8063MUC18 gene exons 1 & 2 X68264 576 661 4692.5 245 SUV39H1 Suppressor ofvariegation 3-9 NM_003173 577 661 5103 1 homolog 1 (Drosophila) 246HSPC048 HSPC048 protein NM_014148 578 660 5981.5 247 OID_5625 EST 3′ endfrom T cells AW063780 579 660 4437 1 248 WARS Tryptophanyl-tRNAsynthetase NM_004184 580 660 905.5 249 OID_6823 107H8 AL832642 581 6592619 250 OID_7073 119F12 AL705961 582 659 6837.5 251 OID_5339 EST 3′ endAI625119 583 658 4414.5 1 252 OID_4263 fetal retina 937202 cDNA cloneAA136584 584 658 5870 IMAGE:565899 253 MGC26766 Hypothetical proteinMGC26766 AK025472 585 658 1892.5 254 SERPINB11 Serine (or cysteine)proteinase NM_080475 586 658 7535.5 1 inhibitor, clade B (ovalbumin),member 11 255 OID_6711 58G4, IMAGE:4359351 5′ BF968628 587 658 7264 256RNF10 Ring finger protein 10 NM_014868 588 658 3127.5 257 MKRN1 Makorin,ring finger protein, 1 NM_013446 589 658 2228.5 258 RPS16 ribosomalprotein S16 NM_001020 590 657 165.5 259 BAZ1A Bromodomain adjacent tozinc finger NM_013448 591 657 2533 domain, 1A 260 OID_5998 EST 3′ endAW468459 592 657 6339.5 261 ATP5L ATP synthase, H+ transporting,NM_006476 593 657 1155 mitochondrial F0 complex, subunit g 262 OID_639352B9 52B9 594 657 7420.5 263 RoXaN Ubiquitous tetratricopeptide BC004857595 656 7378 containing protein RoXaN 264 NCBP2 Nuclear cap bindingprotein subunit NM_007362 596 656 4666.5 2, 20kDa 265 OID_6273 EST 3′end AW294774 597 656 5498.5 266 HZF12 zinc finger protein 12 NM_033204598 656 4715.5 267 CCL3 Chemokine (C-C motif) ligand 3 D90144 599 6564910 1 268 OID_4323 IMAGE:1283731 3′ AA744774 600 655 6406.5 1 269OID_5181 tg93h12.x1 NCI_CGAP_CLL1 AI400725 601 655 4838 1 cDNA cloneIMAGE:2116391 3′ similar to contains TAR1.t1 MER22 270 PRDX4Peroxiredoxin 4 NM_006406 602 655 3397.5 271 BTK Brutonagammaglobulinemia tyrosine NM_000061 603 655 2358 kinase 272 OID_6298Importin beta subunit mRNA AI948513 604 655 2433.5 273 PGK1Phosphoglycerate kinase 1 NM_000291 605 655 2059.5 274 TNFRSF10A Tumornecrosis factor receptor NM_003844 606 654 4897.5 1 superfamily, member10a 275 ADM adrenomedullin NM_001124 607 654 4235 276 OID_357 138G5138G5 608 654 5427.5 1 277 C20orf6 461A4 chromosome 20 open readingNM_016649 609 654 6343 1 frame 6 278 OID_3226 DKFZP564O0823 proteinNM_015393 610 653 6187.5 279 ASAH1 N-acylsphingosine amidohydrolaseNM_004315 611 653 1003 (acid ceramidase) 1 280 ATF5 Activatingtranscription factor 5 NM_012068 612 653 4545.5 281 OID_4887hypothetical protein MGC14376 NM_032895 613 653 2310 1 282 OID_4239 ESTBQ022840 614 652 2774.5 283 MDM2 Mouse double minute 2, homolog of;NM_002392 615 652 4342 p53-binding protein (MDM2), transcript variantMDM2, 284 XRN2 5′-3′ exoribonuclease 2 AF064257 616 652 6896.5 285OID_6039 Endothelial differentiation, BE502246 617 652 5147lysophosphatidic acid G-protein- coupled receptor, 4 (EDG4) 286 OID_4210IMAGE:4540096 AI300700 618 652 1330.5 287 OID_7698 EST 3′ end AA243283619 652 7432.5 1 288 PRKRA Protein kinase, interferon-inducibleNM_003690 620 652 3512.5 double stranded RNA dependent activator 289OID_4288 IMAGE:2091815 AI378046 621 651 6401.5 290 OID_5620 EST 3′ endfrom T cells AW063678 622 651 6400 291 OID_7384 EST 5′ BF475239 623 6516875 292 OID_1209 EST Weakly similar to hypothetical C14379 624 6511356.5 1 protein FLJ20378 293 CDKN1B Cyclin-dependent kinase inhibitor1B NM_004064 625 650 4272.5 (p27, Kip1) 294 PLOD Procollagen-lysine,2-oxoglutarate 5- NM_000302 626 650 3101 dioxygenase (lysinehydroxylase, Ehlers-Danlos syndrome type VI) 295 OID_5128 EST AK097845627 650 6476 296 OID_5877 EST 3′ end AW297664 628 650 6864.5 1 297 FZD4Frizzled (Drosophila) homolog 4 NM_012193 629 650 5816 298 HLA-B Majorhistocompatibility complex, NM_005514 630 650 229 class I, B 299OID_5624 EST 3′ end from T cells AW063921 631 649 7812.5 300 FPR1 Formylpeptide receptor 1 NM_002029 632 649 1156.5 301 ODF2 Outer dense fiberof sperm tails 2 NM_153437 633 649 4982.5 302 OID_5150 tg04g01.x1 cDNA,3′ end AI392793 634 649 7638 /clone = IMAGE:2107824 303 OID_5639 EST 3′end from T cells AW064243 635 648 6805 1 304 OID_6619 469A10 469A10 636647 7110 1 305 OID_6933 463C7, 4 EST hits. Aligned AI089520 637 6476880.5 1 306 OID_7049 480E2 480E2 638 647 7128.5 307 IL17C Interleukin17C NM_013278 639 647 6411.5 308 OID_5866 EST 3′ end BM684739 640 6476532 1 309 CD44 CD44 AA916990 641 646 4758 310 VPS45A Vacuolar proteinsorting 45A (yeast) NM_007259 642 646 3371 311 OID_4932 aa92c03.r1Stratagene fetal retina AA457757 643 646 6057 1 937202 cDNA cloneIMAGE:838756 312 OID_7821 EST AA743221 644 645 7507 313 OID_4916zr76a03.r1 Soares_NhHMPu_S1 AA252909 645 645 6962.5 1 cDNA cloneIMAGE:669292 314 OID_4891 Hypothetical protein LOC255488 AL832329 646645 6148.5 315 HADHB Hydroxyacyl-Coenzyme A NM_000183 647 645 3212.5dehydrogenase/3-ketoacyl-Coenzyme A thiolase/enoyl-Coenzyme A hydratase(trifunctional protein), beta subunit 316 FLJ22757 Hypothetical proteinFLJ22757 NM_024898 648 644 1965.5 1 317 RAC1 Ras-related C3 botulinumtoxin AK054993 649 644 1533 substrate 1 (rho family, small GTP bindingprotein Rac1) 318 OID_6415 72D4, FLJ00290 protein CA407201 650 644 4881319 NMES1 Normal mucosa of esophagus specific 1 NM_032413 651 644 6217 1320 DMBT1 Deleted in malignant brain tumors 1, NM_007329 652 644 7284transcript variant 2 321 RPS23 ribosomal protein S23 NM_001025 653 643219.5 322 ZF HCF-binding transcription factor NM_021212 654 643 4069Zhangfei 323 NFE2L3 Nuclear factor (erythroid-derived 2)- NM_004289 655643 3378 like 3 324 RAD9 RAD9 homolog (S. pombe) NM_004584 656 643 6453325 OID_6295 EST 3′ end AI880607 657 643 7493.5 326 DEFCAP Deatheffector filament-forming Ced- NM_014922 658 643 3059 4-like apoptosisprotein, transcript variant B 327 RPL27A Ribosomal protein L27a BF214146659 642 6571 1 328 IL22 Interleukin 22 (IL22) NM_020525 660 642 3891 1329 PSMA4 Proteasome (prosome, macropain) NM_002789 661 641 1934.5subunit, alpha type, 4, (PSMA4) 330 CCNI cyclin I (CCNI) NM_006835 662641 980.5 331 THBD Thrombomodulin NM_000361 663 640 4732.5 332 CGR19Cell growth regulatory with ring NM_006568 664 640 5510 finger domainPCR PCR PCR PCR Forward Reverse PCR Forward Reverse PCR SEQ ID SEQ IDPrimer 1 Primer 1 Probe 1 Primer 2 Primer 2 Probe 2 Gene 50mer RNA/cDNASEQ ID SEQ ID SEQ ID SEQ ID SEQ ID SEQ ID HSRRN18S 1 333 665 996 1327ACTB 2 334 666 997 1328 GUSB 3 335 667 998 1329 1656 1904 2152 B2M 4 336668 999 1330 TSN 5 337 669 1000 1331 1657 1905 2153 CCR7 6 338 670 10011332 IL1R2 7 339 671 1002 1333 1658 1906 2154 AIF-1 8 340 672 1003 1334ALAS2 9 341 673 1004 1335 APELIN 10 342 674 1005 1336 CD80 11 343 6751006 1337 1659 1907 2145 EPB41 12 344 676 1007 1338 CBLB 13 345 677 10081339 1660 1908 2156 CCR5 14 346 678 1009 1340 1661 1909 2157 MME 15 347679 1010 1341 1662 1910 2158 KLRC1 16 348 680 1011 1342 1663 1911 2159FCGR3A 17 349 681 1012 1343 FCGR3B 18 350 682 1013 1344 1664 1912 2160LAG3 19 351 683 1014 1345 1665 1913 2161 PECAM1 20 352 684 1015 13461666 1914 2162 CD34 21 353 685 1016 1347 1667 1915 2163 FCGR1A 22 354686 1017 1348 1668 1916 2164 TFRC 23 355 687 1018 1349 CMA1 24 356 6881019 1350 1669 1917 2165 KIT 25 357 689 1020 1351 MPL 26 358 690 10211352 1670 1918 2166 EphB6 27 359 691 1022 1353 EPOR 28 360 692 1023 1354Foxp3 29 361 693 1024 1355 1671 1919 2167 GATA-1 30 362 694 1025 1356ITGA2B 31 363 695 1026 1357 1672 1920 2168 GNLY 32 364 696 1027 13581673 1921 2169 GZMA 33 365 697 1028 1359 1674 1922 2170 HBA 34 366 6981029 1360 1675 1923 2171 HBZ 35 367 699 1030 1361 1676 1924 2172 HBB 36368 700 1031 1362 1677 1925 2173 HBD 37 369 701 1032 1363 1678 1926 2174HBE 38 370 702 1033 1364 1679 1927 2175 HBG 39 371 703 1034 1365 16801928 2176 HBQ 40 372 704 1035 1366 1681 1929 2177 HLA-DP 41 373 705 10361367 1682 1930 2178 HLA-DQ 42 374 706 1037 1368 1683 1931 2179 HLA-DRB43 375 707 1038 1369 1684 1932 2180 ICOS 44 376 708 1039 1370 1685 19332181 IL18 45 377 709 1040 1371 1686 1934 2182 IL3 46 378 710 1041 13721687 1935 2183 ITGA4 47 379 711 1042 1373 ITGAM 48 380 712 1043 13741688 1936 2184 ITGB7 49 381 713 1044 1375 CEBPB 50 382 714 1045 13761689 1937 2185 NF-E2 51 383 715 1046 1377 PDCD1 52 384 716 1047 13781690 1938 2186 PF4 53 385 717 1048 1379 1691 1939 2187 PRKCQ 54 386 7181049 1380 1692 1940 2188 PPARGC1 55 387 719 1050 1381 RAG1 56 388 7201051 1382 1693 1941 2189 RAG2 57 389 721 1052 1383 1694 1942 2190 CXCL1258 390 722 1053 1384 1695 1943 2191 TNFRSF4 59 391 723 1054 1385 16961944 2192 TNFSF4 60 392 724 1055 1386 1697 1945 2193 TPS1 61 393 7251056 1387 1698 1946 2194 ADA 62 394 726 1057 1388 1699 1947 2195 CPM 63395 727 1058 1389 1700 1948 2196 CSF2 64 396 728 1059 1390 1701 19492197 CSF3 65 397 729 1060 1391 1702 1950 2198 CRP 66 398 730 1061 13921703 1951 2199 FLT3 67 399 731 1062 1393 1704 1952 2200 GATA3 68 400 7321063 1394 1705 1953 2201 IL7R 69 401 733 1064 1395 1706 1954 2202 KLF170 402 734 1065 1396 1707 1955 2203 LCK 71 403 735 1066 1397 1708 19562204 LEF1 72 404 736 1067 1398 1709 1957 2205 PLAUR 73 405 737 1068 13991710 1958 2206 TNFSF13B 74 406 738 1069 1400 1711 1959 2207 IL8 75 407739 1070 1401 GZMB 76 408 740 1071 1402 TNFSF6 77 409 741 1072 1403TCIRG1 78 410 742 1073 1404 PRF1 79 411 743 1074 1405 IL4 80 412 7441075 1406 IL13 81 413 745 1076 1407 CTLA4 82 414 746 1077 1408 CD8A 83415 747 1078 1409 BY55 84 416 748 1079 1410 OID_4460 85 417 749 10801411 HBB 86 418 750 1081 1412 BPGM 87 419 751 1082 1413 MTHFD2 88 420752 1083 1414 TAP1 89 421 753 1084 1415 KPNA6 90 422 754 1085 1416OID_4365 91 423 755 1086 1417 IGHM 92 424 756 1087 1418 OID_573 93 425757 1088 1419 1712 1960 2208 OID_873 94 426 758 1089 1420 OID_3 95 427759 1090 1421 CXCR4 96 428 760 1091 1422 CD69 97 429 761 1092 1423 CCL598 430 762 1093 1424 IL6 99 431 763 1094 1425 IL2 100 432 764 1095 1426KLRF1 101 433 765 1096 1427 LYN 102 434 766 1097 1428 IL2RA 103 435 7671098 1429 CCL4 104 436 768 1099 1430 OID_6207 105 437 769 1100 1431 ChGn106 438 770 1101 1432 OID_4281 107 439 771 1102 1433 CXCL9 108 440 7721103 1434 CXCL10 109 441 773 1104 1435 IL17 110 442 774 1105 1436 IL15111 443 775 1106 1437 IL10 112 444 776 1107 1438 IFNG 113 445 777 11081439 1713 1961 2209 HLA-DRB1 114 446 778 1109 1440 1714 1962 2210 CD8B1115 447 779 1110 1441 CD4 116 448 780 1111 1442 CXCR3 117 449 781 11121443 OID_7094 118 450 782 1113 1444 OID_7605 119 451 783 1114 1445 CXCL1120 452 784 1115 1446 OID_253 121 453 785 1116 1447 GPI 122 454 786 11171448 CD47 123 455 787 1118 1449 HLA-F 124 456 788 1119 1450 OID_5350 125457 789 1120 1451 TCRGC2 126 458 790 1121 1452 OID_7016 127 459 791 1122PTGS2 128 460 792 1123 1454 OID_5847 129 461 793 1124 1455 PRDM1 130 462794 1125 1456 CKB 131 463 795 1126 1457 TNNI3 132 464 796 1127 1458TNNT2 133 465 797 1128 1459 MB 134 466 798 1129 1460 SLC7A11 135 467 7991130 1461 TNFRSF5 136 468 800 1131 1462 1715 1963 2211 TNFRSF7 137 469801 1132 1463 CD86 138 470 802 1133 1464 AIF1v2 139 471 803 1134 1465 EVBCLF-1 140 472 804 1135 1466 1716 1964 2212 EV EBV 141 473 805 1136 14671717 1965 2213 CMV p67 142 474 806 1137 1468 1718 1966 2214 CMV TRL7 143475 807 1138 1469 1719 1967 2215 CMV IE1e3 144 476 808 1139 1470 17201968 2216 CMV IE1e4 145 477 809 1140 1471 1721 1969 2217 EV EBNA-1 146478 810 1141 1472 1722 1970 2218 EV BZLF-1 147 479 811 1142 1473 17231971 2219 EV EBN 148 480 812 1143 1474 1724 1972 2220 EV EBNA-LP 149 481813 1144 1475 CMV IE1 150 482 814 1145 1476 1725 1973 2221 CMV IE1 151483 815 1146 1477 CLC 152 484 816 1147 1478 1726 1974 2222 TERF2IP 153485 817 1148 1479 1727 1975 2223 HLA-A 154 486 818 1149 1480 1728 19762224 OID_5891 155 487 819 1150 1481 1729 1977 2225 MSCP 156 488 820 11511482 1730 1978 2226 DUSP5 157 489 821 1152 1483 1731 1979 2227 PRO1853158 490 822 1153 1484 1732 1980 2228 OID_6420 159 491 823 1154 1485 17331981 2229 CDSN 160 492 824 1155 1486 1734 1982 2230 OID_4269 161 493 8251156 1487 1735 1983 2231 RPS25 162 494 826 1157 1488 1736 1984 2232 GAPD163 495 827 1158 1489 1737 1985 2233 RPLP1 164 496 828 1159 1490 17381986 2234 OID_5115 165 497 829 1160 1491 1739 1987 2235 SLC9A8 166 498830 1161 1492 1740 1988 2236 OID_1512 167 499 831 1162 1493 1741 19892237 POLR2D 168 500 832 1163 1494 1742 1990 2238 ARPC3 169 501 833 11641495 1743 1991 2239 OID_6282 170 502 834 1165 1496 1744 1992 2240PRO1073 171 503 835 1166 1497 1745 1993 2241 OID_7222 172 504 836 11671498 1746 1994 2242 FPRL1 173 505 837 1168 1499 1747 1995 2243 FKBPL 174506 838 1169 1500 1748 1996 2244 PREB 175 507 839 1170 1501 1749 19972245 OID_1551 176 508 840 1171 1502 1750 1998 2246 OID_7595 177 509 8411172 1503 1751 1999 2247 RNF19 178 510 842 1173 1504 1752 2000 2248 SMCY179 511 843 1174 1505 1753 2001 2249 OID_4184 180 512 844 1175 1506 17542002 2250 OID_7504 181 513 845 1176 1507 1755 2003 2251 DNAJC3 182 514846 1177 1508 1756 2004 2252 ARHU 183 515 847 1178 1509 1757 2005 2253OID_7200 184 516 848 1179 1510 1758 2006 2254 SERPINB2 185 517 849 11801511 ENO1 186 518 850 1181 1512 1759 2007 2255 OID_7696 187 519 851 11821513 1760 2008 2256 OID_4173 188 520 852 1183 1514 1761 2009 2257 CSF2RB189 521 853 1184 1515 1762 2010 2258 OID_7410 190 522 854 1185 1516 17632011 2259 OID_4180 191 523 855 1186 1517 1764 2012 2260 OID_5101 192 524856 1187 1518 1765 2013 2261 MOP3 193 525 857 1188 1519 1766 2014 2262RPL18A 194 526 858 1189 1520 1767 2015 2263 INPP5A 195 527 859 1190 15211768 2016 2264 hIAN7 196 528 860 1191 1522 1769 2017 2265 RPS29 197 529861 1192 1523 1770 2018 2266 OID_6008 198 530 862 1193 1524 1771 20192267 OID_4186 199 531 863 1194 1525 1772 2020 2268 VNN2 200 532 864 11951526 1773 2021 2269 OID_7703 201 533 865 1196 1527 1774 2022 2270OID_7057 202 534 866 1197 1528 1775 2023 2271 OID_4291 203 535 867 11981529 1776 2024 2272 OID_1366 204 536 868 1199 1530 1777 2025 2273 EEF1A1205 537 869 1200 1531 1778 2026 2274 PA2G4 206 538 870 1201 1532 17792027 2275 GAPD 207 539 871 1202 1533 1780 2028 2276 CHD4 208 540 8721203 1534 1781 2029 2277 OID_7951 209 541 873 1204 1535 1782 2030 2278DAB1 210 542 874 1205 1536 1783 2031 2279 OID_3406 211 543 875 1206 15371784 2032 2280 OID_6986 212 544 876 1207 1538 1785 2033 2281 OID_5962213 545 877 1208 1539 1786 2034 2282 OID_5152 214 546 878 1209 1540 17872035 2283 S100A8 215 547 879 1210 1541 1788 2036 2284 HNRPU 216 548 8801211 1542 1789 2037 2285 ERCC5 217 549 881 1212 1543 1790 2038 2286RPS27 218 550 882 1213 1544 1791 2039 2287 ACRC 219 551 883 1214 15451792 2040 2288 PSMD11 220 552 884 1215 1546 1793 2041 2289 OID_1016 221553 885 1216 1547 1794 2042 2290 OID_1309 222 554 886 1217 1548 17952043 2291 OID_7582 223 555 887 1218 1549 1796 2044 2292 OID_4317 224 556888 1219 1550 1797 2045 2293 OID_5889 225 557 889 1220 1551 1798 20462294 UBL1 226 558 890 1221 1552 1799 2047 2295 OID_3687 227 559 891 12221553 1800 2048 2296 OID_7371 228 560 892 1223 1554 1801 2049 2297SH3BGRL3 229 561 893 1224 1555 1802 2050 2298 SEMA7A 230 562 894 12251556 1803 2051 2299 OID_5708 231 563 895 1226 1557 1804 2052 2300OID_5992 232 564 896 1227 1558 1805 2053 2301 IL21 233 565 897 1228 15591806 2054 2302 HERC3 234 566 898 1229 1560 1807 2055 2303 OID_7799 235567 899 1230 1561 1808 2056 2304 P11 236 568 900 1231 1562 1809 20572305 OID_7766 237 569 901 1232 1563 1810 2058 2306 TIMM10 238 570 9021233 1564 1811 2059 2307 EGLN1 239 571 903 1234 1565 1812 2060 2308 TBCC240 572 904 1235 1566 1813 2061 2309 RNF3 241 573 905 1236 1567 18142062 2310 OID_6451 242 574 906 1237 1568 1815 2063 2311 CCNDBP1 243 575907 1238 1569 1816 2064 2312 OID_8063 244 576 908 1239 1570 1817 20652313 SUV39H1 245 577 909 1240 1571 1818 2066 2314 HSPC048 246 578 9101241 1572 1819 2067 2315 OID_5625 247 579 911 1242 1573 1820 2068 2316WARS 248 580 912 1243 1574 1821 2069 2317 OID_6823 249 581 913 1244 15751822 2070 2318 OID_7073 250 582 914 1245 1576 1823 2071 2319 OID_5339251 583 915 1246 1577 1824 2072 2320 OID_4263 252 584 916 1247 1578 18252073 2321 MGC26766 253 585 917 1248 1579 1826 2074 2322 SERPINB11 254586 918 1249 1580 1827 2075 2323 OID_6711 255 587 919 1250 1581 18282076 2324 RNF10 256 588 920 1251 1582 1829 2077 2325 MKRN1 257 589 9211252 1583 1830 2078 2326 RPS16 258 590 922 1253 1584 1831 2079 2327BAZ1A 259 591 923 1254 1585 1832 2080 2328 OID_5998 260 592 924 12551586 1833 2081 2329 ATP5L 261 593 925 1256 1587 1834 2082 2330 OID_6393262 594 926 1257 1588 RoXaN 263 595 927 1258 1589 1835 2083 2331 NCBP2264 596 928 1259 1590 1836 2084 2332 OID_6273 265 597 929 1260 1591 18372085 2333 HZF12 266 598 930 1261 1592 1838 2086 2334 CCL3 267 599 9311262 1593 1839 2087 2335 OID_4323 268 600 932 1263 1594 1840 2088 2336OID_5181 269 601 PRDX4 270 602 933 1264 1595 1841 2089 2337 BTK 271 603934 1265 1596 1842 2090 2338 OID_6298 272 604 935 1266 1597 1843 20912339 PGK1 273 605 936 1267 1598 1844 2092 2340 TNFRSF10A 274 606 9371268 1599 1845 2093 2341 ADM 275 607 938 1269 1600 1846 2094 2342OID_357 276 608 939 1270 1601 1847 2095 2343 C20orf6 277 609 940 12711602 1848 2096 2344 OID_3226 278 610 941 1272 1603 1849 2097 2345 ASAH1279 611 942 1273 1604 1850 2098 2346 ATF5 280 612 943 1274 1605 18512099 2347 OID_4887 281 613 944 1275 1606 1852 2100 2348 OID_4239 282 614945 1276 1607 1853 2101 2349 MDM2 283 615 946 1277 1608 1854 2102 2350XRN2 284 616 947 1278 1609 1855 2103 2351 OID_6039 285 617 948 1279 16101856 2104 2352 OID_4210 286 618 949 1280 1611 1857 2105 2353 OID_7698287 619 950 1281 1612 1858 2106 2354 PRKRA 288 620 951 1282 1613 18592107 2355 OID_4288 289 621 952 1283 1614 1860 2108 2356 OID_5620 290 622953 1284 1615 1861 2109 2357 OID_7384 291 623 954 1285 1616 1862 21102358 OID_1209 292 624 955 1286 1617 1863 2111 2359 CDKN1B 293 625 9561287 1618 1864 2112 2360 PLOD 294 626 957 1288 1619 1865 2113 2361OID_5128 295 627 958 1289 1620 1866 2114 2362 OID_5877 296 628 959 12901621 1867 2115 2363 FZD4 297 629 960 1291 1622 1868 2116 2364 HLA-B 298630 961 1292 1623 1869 2117 2365 OID_5624 299 631 962 1293 1624 18702118 2366 FPR1 300 632 963 1294 1625 1871 2119 2367 ODF2 301 633 9641295 1626 1872 2120 2368 OID_5150 302 634 965 1296 1627 1873 2121 2369OID_5639 303 635 966 1297 1628 1874 2122 2370 OID_6619 304 636 967 12981629 1875 2123 2371 OID_6933 305 637 968 1299 1630 1876 2124 2372OID_7049 306 638 969 1300 1631 1877 2125 2373 IL17C 307 639 970 13011632 1878 2126 2374 OID_5866 308 640 971 1302 1633 1879 2127 2375 CD44309 641 972 1303 1634 1880 2128 2376 VPS45A 310 642 973 1304 1635 18812129 2377 OID_4932 311 643 974 1305 1636 1882 2130 2378 OID_7821 312 644975 1306 1637 1883 2131 2379 OID_4916 313 645 976 1307 1638 1884 21322380 OID_4891 314 646 977 1308 1639 1885 2133 2381 HADHB 315 647 9781309 1640 1886 2134 2382 FLJ22757 316 648 979 1310 1641 1887 2135 2383RAC1 317 649 980 1311 1642 1888 2136 2384 OID_6415 318 650 981 1312 16431889 2137 2385 NMES1 319 651 982 1313 1644 1890 2138 2386 DMBT1 320 652983 1314 1645 1891 2139 2387 RPS23 321 653 984 1315 1646 1892 2140 2388ZF 322 654 985 1316 1647 1893 2141 2389 NFE2L3 323 655 986 1317 16481894 2142 2390 RAD9 324 656 987 1318 1649 1895 2143 2391 OID_6295 325657 988 1319 1650 1896 2144 2392 DEFCAP 326 658 989 1320 1651 1897 21452393 RPL27A 327 659 990 1321 1652 1898 2146 2394 IL22 328 660 991 13221653 1899 2147 2395 PSMA4 329 661 992 1323 1654 1900 2148 2396 CCNI 330662 993 1324 1655 1901 2149 2397 THBD 331 663 994 1325 1656 1902 21502398 CGR19 332 664 995 1326 1657 1903 2151 2399 Non- SEQ ID SEQ IDparametric Fisher t-test Gene Gene Name 50mer RNA/cDNA n Odds ratiop-value p-value HBB Hemoglobin, beta 86 418 55 8.33 0.00 0.00 OID_4365Mitochondrial solute 91 423 53 6.16 0.00 0.00 carrier OID_873 KIAA1892protein 94 426 55 5.09 0.01 0.01 IL4 Interleukin 4 80 412 46 4.90 0.020.01 OID_4281 EST 107 439 56 5.19 0.01 0.01 IGHM Immunoglobulin heavy 92424 52 2.89 0.09 0.01 constant mu BPGM 2,3-bisphosphoglycerate 87 419 437.31 0.01 0.01 mutase CTLA4 Cytotoxic T-lymphocyte- 82 414 52 1.84 0.02associated protein 4 SLC7A11 Solute carrier family 7, 135 467 48 2.500.15 0.03 (cationic amino acid transporter, y+ system) member 11 IL13Interleukin 13 81 413 29 4.95 0.07 0.04 OID_6207 EST 105 437 37 3.580.10 0.04 PRDM1 PR domain containing 1, 130 462 57 1.44 0.07 with ZNFdomain LYN v-yes-1 Yamaguchi 102 434 55 1.08 0.08 sarcoma viral relatedoncogene homolog KPNA6 Karyopherin alpha 6 90 422 51 1.50 0.09 (importinalpha 7) OID_7094 XDx EST 479G12 118 450 35 1.13 0.09 IL15 Interleukin15 111 443 51 3.78 0.05 0.09 OID_4460 EST 85 417 47 2.73 0.14 0.10OID_7016 EST 127 459 53 2.14 0.27 0.10 MTHFD2 Methylene 88 420 43 3.500.07 0.11 tetrahydrofolate dehydrogenase (NAD+ dependent),methenyltetrahydrofolate cyclohydrolase TCIRG1 T-cell, immune regulator78 410 57 1.08 0.11 1, ATPase, H+ transporting, lysosomal V0 protein aisoform 3 OID_5847 Hypothetical protein 129 461 45 1.08 0.12 FLJ32919CXCR4 Chemokine (C-X-C motif) 96 428 56 1.29 0.12 CXCR3 Chemokine (C-X-Cmotif) 117 449 54 2.10 0.27 0.12 GPI Glucose phosphate isomer 122 454 571.44 0.60 0.12 KLRF1 Killer cell lectin-like rece 101 433 50 1.68 0.13CCL5 Chemokine (C-C motif) 1 98 430 34 1.96 0.13 CD47 CD47 antigen(Rh-related) 123 455 55 1.45 0.13 IL10 Interleukin 10 112 444 33 1.430.13 OID_253 EST 121 453 26 1.93 0.15 CXCL10 Chemokine (C-X-C motif) 109441 53 1.75 0.16 IFNG Interferon, gamma 113 445 41 1.33 0.16 PRF1Perforin 1 (pore forming 79 411 48 1.20 0.17 IL2 Interleukin 2 100 43233 2.00 0.17 HLA-DRB1 Major histocompatibility 114 446 42 1.50 0.18 IL6Interleukin 6 99 431 49 1.33 0.18 IL2RA Interleukin 2 receptor, 103 43539 2.03 0.34 0.19 alpha OID_573 KIAA1486 protein 93 425 8 3.00 0.19CXCL9 Chemokine (C-X-C 108 440 46 1.71 0.20 motif) ligand 9 (MIG) OID_3EST 95 427 49 2.19 0.20 CD8B1 CD8 antigen, beta 115 447 55 1.21 0.22polypeptide 1 (p37) CD69 CD69 antigen (p60, 97 429 30 1.71 0.23 earlyT-cell activation antigen) OID_7605 EST 119 451 47 3.11 0.08 0.24 TNFSF6Tumor necrosis factor 77 409 54 1.36 0.25 (ligand) superfamily, member 6CXCL1 Chemokine (C-X-C 120 452 20 2.00 0.26 motif) ligand 1 (melanomagrowth stimulating activity, alpha) OID_5350 EST 125 457 49 2.08 0.260.28 CD8A CD8 antigen, alpha 83 415 57 1.39 0.28 polypeptide (p32) CD4CD4 antigen (p55) 116 448 55 1.64 0.28 PTGS2 Prostaglandin- 128 460 462.05 0.37 0.29 endoperoxide synthase 2 (prostaglandin G/H synthase andcyclooxygenase) GZMB Granzyme B (granzyme 76 408 40 1.81 0.33 2,cytotoxic T- lymphocyte-associated serine esterase 1) CCL4 Chemokine(C-C motif) 104 436 53 2.25 0.35 ligand 4, SCYA4 ChGn Chondroitin beta1,4 N- 106 438 31 2.57 0.36 acetylgalactosaminyl- transferase TCRGC2 Tcell receptor gamma 126 458 52 1.33 0.39 constant 2 HLA-F Majorhistocompatibility 124 456 54 2.36 0.17 0.40 complex, class I, F TAP1Transporter 1, ATP- 89 421 36 1.93 0.45 binding cassette, sub- family B(MDR1/TAP) BY55 Natural killer cell 84 416 52 2.49 0.16 0.48 receptor,immunoglobulin superfamily member IL8 Interleukin 8 75 407 49 2.10 0.260.49 SEQ ID SEQ ID RefSeq Peptide SEQ ID Gene ACC 50mer RNA/cDNAAccession # Protein ACTB NM_001101 2 334 NP_001092 2400 GUSB NM_000181 3335 NP_000172 2401 B2M NM_004048 4 336 NP_004039 2402 TSN NM_004622 5337 NP_004613 2403 CCR7 NM_001838 6 338 NP_001829 2404 IL1R2 NM_004633 7339 NP_004624 2405 AIF-1 NM_004847 8 340 NP_004838 2406 ALAS2NM_000032.1 9 341 NP_000023 2407 APELIN NM_017413 10 342 NP_059109 2408CD80 NM_005191 11 343 NP_005182 2409 EPB41 NM_004437 12 344 NP_0044282410 CBLB NM_004351 13 345 NP_733762 2411 CCR5 NM_000579 14 346NP_000570 2412 MME NM_000902 15 347 NP_000893 2413 KLRC1 NM_002259 16348 NP_002250 2414 FCGR3A NM_000569 17 349 NP_000560 2415 FCGR3BNM_000570 18 350 NP_000561 2416 LAG3 NM_002286 19 351 NP_002277 2417PECAM1 NM_000442 20 352 NP_000433 2418 CD34 NM_001773 21 353 NP_0017642419 FCGR1A NM_000566 22 354 NP_000557 2420 TFRC NM_003234 23 355NP_003225 2421 CMA1 NM_001836 24 356 NP_001827 2422 KIT NM_000222 25 357NP_000213 2423 MPL NM_005373 26 358 NP_005364 2424 EphB6 NM_004445 27359 NP_004436 2425 EPO-R NM_000121.2 28 360 NP_000112 2426 Foxp3NM_014009 29 361 NP_054728 2427 GATA-1 NM_002049 30 362 NP_002040 2428ITGA2B NM_000419 31 363 NP_000410 2429 GNLY NM_006433 32 364 NP_0064242430 GZMA NM_006144 33 365 NP_006135 2431 HBA NM_000558.3 34 366NP_000549 2432 HBZ NM_005332.2 35 367 NP_005323 2433 HBD NM_000519.2 37369 NP_000510 2434 HBE NM_005330 38 370 NP_005321 2435 HBG NM_000559.239 371 NP_000550 2436 HBQ NM_005331 40 372 NP_005322 2437 HLA-DPNM_033554 41 373 NP_291032 2438 HLA-DQ NM_002122 42 374 NP_002113 2439ICOS NM_012092 44 376 NP_036224 2440 IL18 NM_001562 45 377 NP_0015532441 IL3 NM_000588 46 378 NP_000579 2442 ITGA4 NM_000885 47 379NP_000876 2443 ITGAM NM_000632 48 380 NP_000623 2444 ITGB7 NM_000889 49381 NP_000880 2445 CEBPB NM_005194 50 382 NP_005185 2446 NF-E2 NM_00616351 383 NP_006154 2447 PDCD1 NM_005018 52 384 NP_005009 2448 PF4NM_002619 53 385 NP_002610 2449 PRKCQ NM_006257.1 54 386 NP_006248 2450PPARGC1 NM_013261 55 387 NP_037393 2451 RAG1 NM_000448 56 388 NP_0004392452 RAG2 NM_000536 57 389 NP_000527 2453 CXCL12 NM_000609 58 390NP_000600 2454 TNFRSF4 NM_003327 59 391 NP_003318 2455 TNFSF4 NM_00332660 392 NP_003317 2456 TPS1 NM_003293 61 393 NP_003284 2457 ADA NM_00002262 394 NP_000013 2458 CPM NM_001874.1 63 395 NP_001865 2459 CSF2NM_000758.2 64 396 NP_000749 2460 CSF3 NM_172219 65 397 NP_757373 2461CRP NM_000567.1 66 398 NP_000558 2462 FLT3 NM_004119 67 399 NP_0041102463 GATA3 NM_002051.1 68 400 NP_002042 2464 IL7R NM_002185.1 69 401NP_002176 2465 KLF1 NM_006563.1 70 402 NP_006554 2466 LCK NM_005356.2 71403 NP_005347 2467 LEF1 NM_016269.2 72 404 NP_057353 2468 PLAURNM_002659.1 73 405 NP_002650 2469 TNFSF13B NM_006573.3 74 406 NP_0065642470 IL8 NM_000584 75 407 NP_000575 2471 GZMB NM_004131 76 408 NP_0041222472 TNFSF6 NM_000639 77 409 NP_000630 2473 TCIRG1 NM_006019 78 410NP_006010 2474 PRF1 NM_005041 79 411 NP_005032 2475 IL4 NM_000589 80 412NP_000580 2476 IL13 NM_002188 81 413 NP_002179 2477 CTLA4 NM_005214 82414 NP_005205 2478 CD8A NM_001768 83 415 NP_001759 2479 BY55 NM_00705384 416 NP_008984 2480 HBB NM_000518 86 418 NP_000509 2481 BPGM NM_00172487 419 NP_001715 2482 MTHFD2 NM_006636 88 420 NP_006627 2483 TAP1NM_000593 89 421 NP_000584 2484 OID_873 AK000354 94 426 NP_056212 2485CXCR4 NM_003467 96 428 NP_003458 2486 CD69 NM_001781 97 429 NP_0017722487 CCL5 NM_002985 98 430 NP_002976 2488 IL6 NM_000600 99 431 NP_0005912489 IL2 NM_000586 100 432 NP_000577 2490 KLRF1 NM_016523 101 433NP_057607 2491 LYN NM_002350 102 434 NP_002341 2492 IL2RA NM_000417 103435 NP_000408 2493 CCL4 NM_002984 104 436 NP_002975 2494 ChGn NM_018371106 438 NP_060841 2495 CXCL9 NM_002416 108 440 NP_002407 2496 CXCL10NM_001565 109 441 NP_001556 2497 IL17 NM_002190 110 442 NP_002181 2498IL15 NM_000585 111 443 NP_000576 2499 IL10 NM_000572 112 444 NP_0005632500 IFNG NM_000619 113 445 NP_000610 2501 HLA-DRB1 NM_002124 114 446NP_002115 2502 CD8B1 NM_004931 115 447 NP_004922 2503 CD4 NM_000616 116448 NP_000607 2504 CXCR3 NM_001504 117 449 NP_001495 2505 CXCL1NM_001511 120 452 NP_001502 2506 GPI NM_000175 122 454 NP_000166 2507CD47 NM_001777 123 455 NP_001768 2508 HLA-F NM_018950 124 456 NP_0618232509 PTGS2 NM_000963 128 460 NP_000954 2510 OID_5847 NM_144588 129 461NP_653189 2511 PRDM1 NM_001198 130 462 NP_001189 2512 CKB NM_001823 131463 NP_001814 2513 TNNI3 NM_000363 132 464 NP_000354 2514 TNNT2NM_000364 133 465 NP_000355 2515 MB NM_005368 134 466 NP_005359 2516SLC7A11 NM_014331 135 467 NP_055146 2517 TNFRSF5 NM_001250 136 468NP_001241 2518 TNFRSF7 NM_001242 137 469 NP_001233 2519 CD86 NM_175862138 470 NP_787058 2520 AIF1v2 NM_004847 139 471 NP_004838 2521 CMV IE1e3NC_001347, compl 144 476 NP_040060 2522 CMV IE1e4 NC_001347, compl 145477 NP_040060 2523 EV EBNA-1 NC_001345, 10795 146 478 NP_039875 2524 EVBZLF-1 NC_001345, compl 147 479 NP_039871 2525 CMV IE1 NC_001347, compl150 482 NP_040060 2526 CMV IE1 NC_001347, compl 151 483 NP_040060 2527CLC NM_001828 152 484 NP_001819 2528 TERF2IP NM_018975 153 485 NP_0618482529 HLA-A NM_002116 154 486 NP_002107 2530 MSCP NM_018579 156 488NP_061049 2531 DUSP5 NM_004419 157 489 NP_004410 2532 PRO1853 NM_018607158 490 NP_061077 2533 CDSN NM_001264 160 492 NP_001255 2534 RPS25NM_001028 162 494 NP_001019 2535 GAPD NM_002046 163 495 NP_002037 2536RPLP1 NM_001003 164 496 NP_000994 2537 POLR2D NM_004805 168 500NP_004796 2538 ARPC3 NM_005719 169 501 NP_005710 2539 FPRL1 NM_001462173 505 NP_001453 2540 FKBPL NM_022110 174 506 NP_071393 2541 PREBNM_013388 175 507 NP_037520 2542 OID_7595 NM_015653 177 509 NP_0564682543 RNF19 NM_015435 178 510 NP_056250 2544 SMCY NM_004653 179 511NP_004644 2545 OID_7504 NM_152312 181 513 NP_689525 2546 DNAJC3NM_006260 182 514 NP_006251 2547 ARHU NM_021205 183 515 NP_067028 2548OID_7200 NM_022752 184 516 NP_073589 2549 SERPINB2 NM_002575 185 517NP_002566 2550 ENO1 NM_001428 186 518 NP_001419 2551 MOP3 NM_018183 193525 NP_060653 2552 RPL18A NM_000980 194 526 NP_000971 2553 INP_P5ANM_005539 195 527 NP_005530 2554 RPS29 NM_001032 197 529 NP_001023 2555VNN2 NM_004665 200 532 NP_004656 2556 OID_7703 NM_014949 201 533NP_055764 2557 EEF1A1 NM_001402 205 537 NP_001393 2558 PA2G4 NM_006191206 538 NP_006182 2559 GAPD NM_002046 207 539 NP_002037 2560 CHD4NM_001273 208 540 NP_001264 2561 OID_7951 NM_016521 209 541 NP_0576052562 DAB1 NM_021080 210 542 NP_066566 2563 OID_3406 NM_018986 211 543NP_061859 2564 S100A8 NM_002964 215 547 NP_002955 2565 ERCC5 NM_000123217 549 NP_000114 2566 RPS27 NM_001030 218 550 NP_001021 2567 ACRCNM_052957 219 551 NP_443189 2568 UBL1 NM_003352 226 558 NP_003343 2569SH3BGRL3 NM_031286 229 561 NP_112576 2570 SEMA7A NM_003612 230 562NP_003603 2571 IL21 NM_021803 233 565 NP_068575 2572 HERC3 NM_014606 234566 NP_055421 2573 P11 NM_006025 236 568 NP_006016 2574 TIMM10 NM_012456238 570 NP_036588 2575 EGLN1 AJ310543 239 571 NP_071334 2576 TBCCNM_003192 240 572 NP_003183 2577 RNF3 NM_006315 241 573 NP_006306 2578CCNDBP1 NM_012142 243 575 NP_036274 2579 SUV39H1 NM_003173 245 577NP_003164 2580 HSPC048 NM_014148 246 578 NP_054867 2581 WARS NM_004184248 580 NP_004175 2582 SERPINB11 NM_080475 254 586 NP_536723 2583 RNF10NM_014868 256 588 NP_055683 2584 MKRN1 NM_013446 257 589 NP_038474 2585RPS16 NM_001020 258 590 NP_001011 2586 BAZ1A NM_013448 259 591 NP_0384762587 ATP5L NM_006476 261 593 NP_006467 2588 NCBP2 NM_007362 264 596NP_031388 2589 HZF12 NM_033204 266 598 NP_149981 2590 CCL3 D90144 267599 NP_002974 2591 PRDX4 NM_006406 270 602 NP_006397 2592 BTK NM_000061271 603 NP_000052 2593 PGK1 NM_000291 273 605 NP_000282 2594 TNFRSF10ANM_003844 274 606 NP_003835 2595 ADM NM_001124 275 607 NP_001115 2596C20orf6 NM_016649 277 609 NP_057733 2597 OID_3226 NM_015393 278 610NP_056208 2598 ASAH1 NM_004315 279 611 NP_004306 2599 ATF5 NM_012068 280612 NP_036200 2600 OID_4887 NM_032895 281 613 NP_116284 2601 MDM2NM_002392 283 615 NP_002383 2602 XRN2 AF064257 284 616 NP_036387 2603PRKRA NM_003690 288 620 NP_003681 2604 CDKN1B NM_004064 293 625NP_004055 2605 PLOD NM_000302 294 626 NP_000293 2606 FZD4 NM_012193 297629 NP_036325 2607 HLA-B NM_005514 298 630 NP_005505 2608 FPR1 NM_002029300 632 NP_002020 2609 ODF2 NM_153437 301 633 NP_702915 2610 IL17CNM_013278 307 639 NP_037410 2611 VPS45A NM_007259 310 642 NP_009190 2612HADHB NM_000183 315 647 NP_000174 2613 FLJ22757 NM_024898 316 648NP_079174 2614 NMES1 NM_032413 319 651 NP_115789 2615 DMBT1 NM_007329320 652 NP_015568 2616 RPS23 NM_001025 321 653 NP_001016 2617 ZFNM_021212 322 654 NP_067035 2618 NFE2L3 NM_004289 323 655 NP_004280 2619RAD9 NM_004584 324 656 NP_004575 2620 DEFCAP NM_014922 326 658 NP_0557372621 IL22 NM_020525 328 660 NP_065386 2622 PSMA4 NM_002789 329 661NP_002780 2623 CCNI NM_006835 330 662 NP_006826 2624 THBD NM_000361 331663 NP_000352 2625 CGR19 NM_006568 332 664 NP_006559 2626 HSRRN18SX03205 1 333 HBB NG_000007 36 368 HLA-DRB 43 375 OID_4460 AF150295 85417 KPNA6 AW021037 90 422 OID_4365 AI114652 91 423 IGHM BC032249 92 424OID_573 AB040919 93 425 OID_3 AW968823 95 427 OID_6207 D20522 105 437OID_4281 AA053887 107 439 OID_7094 118 450 OID_7605 AA808018 119 451OID_253 AK091125 121 453 OID_5350 AK055687 125 457 TCRGC2 M17323 126 458OID_7016 B1018696 127 459 EV EBV 141 473 CMV p67 NC_001347 142 474 CMVTRL7 143 475 EV EBN 148 480 EV EBNA-LP 149 481 OID_5891 AW297949 155 487OID_6420 AK090404 159 491 OID_4269 BM727677 161 493 OID_5115 AI364926165 497 SLC9A8 AB023156 166 498 OID_1512 BE618004 167 499 OID_6282BC041913 170 502 PRO1073 AF001542 171 503 OID_7222 BG260891 172 504OID_1551 BE887646 176 508 OID_4184 X17403 180 512 OID_7696 AW297325 187519 OID_4173 X17403 188 520 CSF2RB AL540399 189 521 OID_7410 AW837717190 522 OID_4180 X17403 191 523 OID_5101 BG461987 192 524 hIAN7 BG772661196 528 OID_6008 AW592876 198 530 OID_4186 X17403 199 531 OID_7057 480F8202 534 OID_4291 8C038439 203 535 OID_1366 AW850041 204 536 OID_6986AK093608 212 544 OID_5962 AW452467 213 545 OID_5152 AI392805 214 546HNRPU BM467823 216 548 PSMD11 AI684022 220 552 OID_1016 AK024456 221 553OID_1309 AV706481 222 554 OID_7582 AK027866 223 555 OID_4317 AI318342224 556 OID_5889 AW297843 225 557 OID_3687 W03955 227 559 OID_7371BE730505 228 560 OID_5708 AW081540 231 563 OID_5992 AW467992 232 564OID_7799 AW837717 235 567 OID_7766 AW294711 237 569 OID_6451 AL834168242 574 OID_8063 X68264 244 576 OID_5625 AW063780 247 579 OID_6823AL832642 249 581 OID_7073 AL705961 250 582 OID_5339 AI625119 251 583OID_4263 AA136584 252 584 MGC26766 AK025472 253 585 OID_6711 BF968628255 587 OID_5998 AW468459 260 592 OID_6393 52B9 262 594 RoXaN BC004857263 595 OID_6273 AW294774 265 597 OID_4323 AA744774 268 600 OID_5181AI400725 269 601 OID_6298 AI948513 272 604 OID_357 138G5 276 608OID_4239 BQ022840 282 614 OID_6039 BE502246 285 617 OID_4210 AI300700286 618 OID_7698 AA243283 287 619 OID_4288 AI378046 289 621 OID_5620AW063678 290 622 OID_7384 BF475239 291 623 OID_1209 C14379 292 624OID_5128 AK097845 295 627 OID_5877 AW297664 296 628 OID_5624 AW063921299 631 OID_5150 AI392793 302 634 OID_5639 AW064243 303 635 OID_6619469A10 304 636 OID_6933 AI089520 305 637 OID_7049 480E2 306 638 OID_5866BM684739 308 640 CD44 AA916990 309 641 OID_4932 AA457757 311 643OID_7821 AA743221 312 644 OID_4916 AA252909 313 645 OID_4891 AL832329314 646 RAC1 AK054993 317 649 OID_6415 CA407201 318 650 OID_6295AI880607 325 657 RPL27A BF214146 327 659

TABLE 3 Viral genomes were used to design oligonucleotides for themicroarrays. The accession numbers for the viral genomes used are given,along with the gene name and location of the region used foroligonucleotide design. Virus Gene Name Genome Location Adenovirus, E1a1226 . . . 1542 type 2 E1b_1 3270 . . . .3503 Accession E2a_2complement(24089 . . . 25885) #J01917 E3-1 27609 . . . 29792 E4 (lastexon complement(33193 . . . 32802) at 3'-end) IX 3576 . . . 4034 Iva2complement(4081 . . . 5417) DNA Polymerase complement(5187 . . . 5418)Cytomega- HCMVTRL2 1893 . . . 2240 lovirus (IRL2) (CMV) HCMVTRL7complement(6595 . . . 6843) Accession (IRL7) #X17403 HCMVUL21complement(26497 . . . 27024) HCMVUL27 complement(32831 . . . 34657)HCMVUL33 43251 . . . 44423 HCMVUL54 complement(76903 . . . 80631)HCMVUL75 complement(107901 . . . 110132) HCMVUL83 complement(119352 . .. 121037) HCMVUL106 complement(154947 . . . 155324) HCMVUL109complement(157514 . . . 157810) HCMVUL113 161503 . . . 162800 HCMVUL122complement(169364 . . . 170599) HCMVUL123 complement(171006 . . .172225) (last exon at 3'-end) HCMVUS28 219200 . . . 220171 Epstein-BarrExon in EBNA-1 67477 . . . 67649 virus (EBV) RNA Accession Exon inEBNA-1 98364 . . . 98730 # NC_001345 RNA BRLF1 complement(103366 . . .105183) BZLF1 (first of complement(102655 . . . 103155) 3 exons) BMLF1complement(82743 . . . 84059) BALF2 complement(161384 . . . 164770)Human U16/U17 complement(26259 . . . 27349) Herpesvirus U89complement(133091 . . . 135610) 6 (HHV6) U90 complement(135664 . . .135948) Accession U86 complement(125989 . . . 128136) #NC_001664 U83123528 . . . 123821 U22 complement(33739 . . . 34347) DR2 (DR2L) 791 . .. 2653 DR7 (DR7L) 5629 . . . 6720 U95 142941 . . . 146306 U94complement(141394 . . . 142866) U39 complement(59588 . . . 62080) U42complement(69054 . . . 70598) U81 complement(121810 . . . 122577) U91136485 . . . 136829

TABLE 4 Dependent variables for discovery of gene expression markers ofcardiac allograft rejection. Number of Number of Dependent RejectionNo-Rejection Variable Description Samples Samples 0 vs 1-4 Bx Grade 0vs. Grades 1-4, 65 114 local biopsy reading s0 vs 1B-4 HG Stable Grade 0vs Grades 41 57 1B-4, highest grade, Grade 1A not included 0-1A vs 1B-4Grades 0 and 1A vs Grades 121 58 HG 1B-4, highest grade. 0 vs 3A HGGrade 0 vs Grade 3A, highest 56 29 grade. Grades 1A-2 and Grade 3B werenot included. 0 vs 1B-4 Grade 0 vs Grades 1B-4, 57 57 highest grade.Grade 1A was not included. 0 vs 1A-4 Grade 0 vs. Grades 1-4, 56 123highest grade

TABLE 5 Real-time PCR assay chemistries. Various combinations ofreporter and quencher dyes are useful for real-time PCR assays. ReporterQuencher FAM TAMRA BHQ1 TET TAMRA BHQ1 JOE TAMRA BHQ1 HEX TAMRA BHQ1 VICTAMRA BHQ1 ROX BHQ2 TAMRA BHQ2

TABLE 6 Real-time PCR results for rejection markers Gene Array Probe SEQPhase 1 Phase 2 All Data ID Fold t-Test NR R Fold t-Test NR R Foldt-Test NR R 95 1.093 0.36084 10 8 0.935 0.31648 21 13 111 1.415 0.009512 10 1.415 0.0095 12 10 79 1.822 0.01146 6 7 0.63 0.04185 19 15 0.720.05632 35 26 3016 1.045 0.41017 12 10 1.001 0.49647 16 15 75 0.840.36674 11 8 0.595 0.15788 16 13 0.628 0.08402 34 26 2765 1.653 0.0150810 10 0.776 0.11082 19 14 0.956 0.37421 38 29 97 0.75 0.26201 8 8 0.5430.11489 17 12 2635 1.553 0.00533 13 10 0.834 0.16853 18 15 0.988 0.4619136 27 96 1.495 0.06288 13 9 1.157 0.27601 18 15 1.155 0.21096 33 25 1001.43 0.166 10 5 1.408 0.14418 12 8 2766 0.956 0.43918 12 10 0.9890.48275 19 14 0.978 0.45101 31 24 2726 1.037 0.38205 11 9 1.037 0.3820511 9 2768 1.211 0.02386 9 9 1.211 0.02386 9 9 94 1.601 0.02418 11 101.831 0.00094 17 15 2769 1.133 0.23094 12 9 1.081 0.19632 19 15 1.1010.15032 31 24 2770 1.734 0.00017 13 10 1.381 0.01323 20 15 2647 1.5570.04502 10 8 1.557 0.04502 10 8 2771 1.99 0.05574 13 9 1.52 0.11108 1713 82 2.029 0.00022 8 5 1.287 0.13022 18 14 1.256 0.05356 33 23 83 1.5460.05865 13 10 0.577 0.03934 18 14 0.795 0.11993 39 26 98 0.716 0.13 1915 0.577 0.03352 19 14 36 1.605 0.09781 12 8 2.618 0.01227 18 11 2.8080.00015 38 23 80 5.395 0.00049 9 6 4.404 0.05464 10 10 2.33 0.02369 2918 89 0.295 0.02856 6 6 77 1.894 0.01602 10 10 0.537 0.01516 19 15 0.8630.21987 35 29 2772 1.583 0.06276 10 6 0.714 0.13019 13 10 1.136 0.2884128 17 2773 1.391 0.09236 11 6 1.391 0.09236 11 6 2774 1.59 0.00022 13 101.59 0.00022 13 10 102 1.245 0.05079 11 10 1.018 0.42702 17 15 1.1170.08232 32 28 2775 0.719 0.16243 11 9 0.719 0.16243 11 9 2776 1.2570.0516 12 9 1.257 0.0516 12 9 2667 1.343 0.03806 13 9 1.13 0.15962 20 12115 1.199 0.26299 11 9 1.199 0.26299 11 9 2669 2.146 0.00813 12 10 1.2960.14285 18 12 2777 1.142 0.20245 13 10 1.142 0.20245 13 10 78 1.3240.01985 12 9 0.967 0.33851 18 14 1.007 0.46864 38 24 2670 1.388 0.1120913 9 1.388 0.11209 13 9 88 1.282 0.14267 7 7 0.995 0.48504 17 14 1.0080.47383 30 23 2778 1.128 0.19528 13 9 1.128 0.19528 13 9 2779 1.9910.02513 9 5 0.642 0.05002 18 14 0.868 0.26275 32 21 2780 1.597 0.0035513 10 0.802 0.11649 17 14 1.013 0.45521 38 26 2781 0.492 0.01344 12 120.819 0.25555 17 15 101 0.652 0.04317 19 15 0.773 0.09274 29 22 1061.234 0.19141 13 8 1.234 0.19141 13 8 2683 1.598 0.03723 8 8 0.6330.03893 14 10 0.86 0.18731 28 22 2782 1.213 0.03305 12 10 0.912 0.0746519 15 0.969 0.31955 39 27 87 4.947 0.02192 18 15 3.857 0.00389 30 23 990.639 0.06613 7 5 0.839 0.30304 16 8 0.694 0.04347 27 15 2692 0.8010.21236 12 8 0.893 0.33801 18 15 0.782 0.06938 38 25 104 2.292 0.0024 118 0.621 0.05152 19 15 0.913 0.34506 30 23 76 1.809 0.00893 9 8 0.6930.13027 13 8 1.274 0.11887 28 19 91 1.969 0.07789 11 8 4.047 0.00812 1913 3.535 0.00033 37 23 92 2.859 0.05985 11 8 9.783 0.03047 18 14 8.5880.00192 37 24 85 0.95 0.43363 12 8 0.699 0.0787 13 13 0.633 0.01486 3324 126 1.76 0.02199 11 10 1.76 0.02199 11 10 2783 0.945 0.46023 10 50.852 0.26701 17 10 0.986 0.48609 29 17 2707 1.055 0.31435 13 10 1.0550.31435 13 10 123 1.154 0.11677 11 10 1.154 0.11677 11 10 84 1.7860.00255 9 6 0.523 0.04965 18 14 0.785 0.14976 34 22 2784 2.12 0.00022 1210 0.498 0.01324 18 13 0.935 0.37356 37 25 2785 1.181 0.1377 10 10 1.1810.1377 10 10 124 1.353 0.08122 11 9 1.353 0.08122 11 9 90 1.355 0.0228813 10 0.973 0.39248 15 13 1.125 0.08671 28 23 2786 1.306 0.0773 12 101.306 0.0773 12 10 2787 1.086 0.32378 12 10 1.086 0.32378 12 10 30181.523 0.1487 12 10 0.84 0.27108 18 13 1.101 0.33276 36 26 125 1.2520.05782 11 10 1.252 0.05782 11 10 2788 1.255 0.1221 11 10 1.255 0.122111 10 2789 1.152 0.31252 9 6 1.152 0.31252 9 6 3019 1.268 0.21268 6 70.981 0.45897 16 10 1.012 0.46612 29 19 2790 0.881 0.17766 11 8 1.220.04253 18 10 0.966 0.33826 40 23 2791 1.837 0.00553 13 10 1.837 0.0055313 10 3020 1.271 0.10162 12 10 0.853 0.10567 19 13 0.965 0.36499 36 252792 1.504 0.05096 12 10 0.713 0.02979 19 15 0.846 0.16914 31 25 27931.335 0.03133 12 10 0.883 0.18577 19 15 0.916 0.23865 36 27 2794 1.9360.00176 13 9 0.717 0.09799 19 14 0.877 0.22295 40 25 2752 1.499 0.0307712 8 0.808 0.15363 17 13 1.004 0.48903 36 23 2795 0.815 0.24734 8 50.965 0.41772 19 15 0.938 0.3265 32 22 119 1.272 0.20279 10 10 1.2720.20279 10 10

TABLE 7 Significance analysis for microarrays for identification ofmarkers of acute rejection. In each case the highest grade from the 3pathologists was taken for analysis. No rejection and rejection classesare defined. Samples are either used regardless of redundancy withrespect to patients or a requirement is made that only one sample isused per patient or per patient per class. The number of samples used inthe analysis is given and the lowest FDR achieved is noted. No RejectionRejection # Samples Low FDR All Samples Grade 0 Grade 3A-4 148 1 Grade 0Grade 1B, 3A-4 158 1.5 Non-redundant within class Grade 0 Grade 3A-4 867 Grade 0 Grade 1B, 3A-4 93 16 Non-redundant (1 sample/patient) Grade 0Grade 3A-4 73 11

TABLE 8 Renal rejection tissue gene expression SAM analysis Array probeProtein Leukocyte ID Gene FDR SEQ ID expression Secreted 2697 CD69antigen (p60, early T-cell activat 1.5625 2925 + 2645 Ras association(RaIGDS/AF-6) 1.5625 2926 2707 CD33 antigen (gp67) (CD33), mRNA 1.56252927 + 2679 Ras association (RaIGDS/AF-6) domain fa 1.5625 2928 2717EST, 5 end 1.5625 2646 mRNA for KIAA0209 gene, partial cds/cd 1.56252929 2667 leupaxin (LPXN), mRNA/cds = (93,1253) 1.5625 2930 + 2706 c-EST 3 end/clone = IMAGE: 2.1111 2740 c- insulin induced gene 1 (INSIG1),mRNA 2.2 117 chemokine (C-X-C motif) receptor 3 2.8125 2931 2669IL2-inducible T-cell kinase (ITK), mRNA 2.8125 2932 + 2674 gliomapathogenesis-related protein (RT 2.8125 2933 2743 c- nuclear receptorsubfamily 1, group I 2.8125 326 death effector filament-forming Ced-4-I2.8125 2934 2716 EST cDNA, 3 end 2.8125 2727 c- chemokine (C-X-C motif),receptor 4 3.1316 2935 + 2721 c- EST 3 end/clone = IMAGE: 3.1316 2641hypothetical protein FLJ20647 (FLJ20647 3.1316 2936 2671 tumor necrosisfactor, alpha-induced pr 3.525 2937 2752 protein tyrosine phosphatase,receptor 3.8077 2938 + 2737 7f37g03.x1 cDNA, 3 end/clone = IMAGE: 3.80772719 c- EST372075 cDNA 3.8077 2684 molecule possessing ankyrin repeatsind 3.8077 2939 76 granzyme B (granzyme 2, cytotoxic T-lym 3.80772940 + + 2677 lectin-like NK cell receptor (LLT1), mR 3.8077 2941 + 2748c-107G11 3.9 2703 c- EST, 5 end/clone = IMAGE 3.9 2711 SAM domain, SH3domain and nuclear 3.9 2942 2663 phosphodiesterase 4B, cAMP-specific 3.92943 + 98 small inducible cytokine A5 (RANTES) 4.5645 2944 + + 2657tumor necrosis factor receptor superfam 4.8286 2945 2683 B-celllymphoma/leukaemia 11B (BCL11B) 4.8286 2946 + 2686 phospholipase A2,group VII (platelet-a 4.8286 2947 + 2687 phosphatidylinositol 3-kinasecatalytic 4.8286 2948 2644 AV659177 cDNA, 3 end 4.9028 2664 regulator ofG-protein signalling 10 (R 5.0238 2949 2747 c- integral membrane protein2A (ITM2A), 5.0238 2950 2744 c- interferon consensus sequence binding5.0238 2678 HSPC022 protein (HSPC022), mRNA 5.0238 2951 2731 c-xj98c03.x1 NCI_CGAP Co18 cDNA 5.0238 2713 caspase recruitment domainprotein 9 (L 5.0238 2952 2736 c- small inducible cytokine A4 (homologo5.1395 2953 + + 2708 major histocompatibility complex, class 5.15 2954249 c-107H8 5.15 2670 CD72 antigen (CD72), mRNA 5.15 2955 + 2661 heatshock 70kD protein 6 (HDP70B) 5.15 2956 2680 bridging integrator 2(BIN2), mRNA/cds 5.15 2957 2754 UI-H-BW0-aiy-b-10-0-UI.s1 cDNA,3 end5.15 2728 c- EST380762 cDNA 5.15 174 FKBPL 5.15 2958 2742 c- chromoboxhomolog 3 (DM) 5.15 2668 basement membrane-induced gene(ICB-1) 5.15 29592750 Lysosomal-assoc. multispanning memb 5.15 2960 2746 174D1 5.15 2738c-AV716627 cDNA, 5 end 5.15 2627 solute carrier family 17 (sodium phosph5.15 2961 2739 c- asparaginyl-tRNA synthetase (NARS) 5.15 124 majorhistocompatibility complex, class 5.15 2962 2647 mRNA for T-cellspecific protein/cds 5.15 2963 + 2628 c-EST, 3 end 5.2295 2638 ExpresscDNA library cDNA 5 5.2903 2725 c- 601571679F1 cDNA, 5 end 5.3385 29642714 gg78c05.x1 cDNA, 3 end/clone 5.3385 2965 2635 interleukin 2receptor gamma chain 5.3385 2966 + 2751 7264, lectin,galactoside-binding, soluble 5.4167 2967 + 2629 8, cDNA: FLJ21559 fis,clone COL06406 5.5299 2968 2695 mRNA; cDNA DKFZp434E0516 5.5588 29692741 c- hexokinase 2 (HK2), mRNA 5.5986 41 Similar to majorhistocompatibility antigen 5.5986 2970 2691 CD5 antigen (p56-62) (CD5)5.5986 2971 2726 c- 602650370T1 cDNA, 3 5.6014 2722 c- EST cDNA clone5.6014 2689 interleukin-2 receptor 5.6014 2972 2734 c- nuclear receptorsubfamily 1, group I 5.6667 2631 pre-B-cell colony-enhancing factor5.7566 2973 + 2656 postmeiotic segregation increased 5.7756 2974 2696protein tyrosine phosphatase, receptor 5.7756 2975 2676 butyrophilin,subfamily 3, member A2 5.8165 2976 2701 c- EST 3 end 5.9048 2730 EST 3end/clone = IMAGE 5.9048 2710 high affin. immunoglobulin epsilon recept.5.9048 2977 2632 encoding major histocompatibility comple 5.9048 29782724 c- EST 3 end 5.9048 2698 EST 6.0353 2662 interferon regulatoryfactor 1 (IRF1), 6.0988 2979 139 allograft inflammatory factor 1 (AIF1),6.1379 2980 2753 platelet activating receptor homolog (H 6.3182 29812704 c- EST 3 end/clone = IMAGE: 7.0337 2675 pim-2 oncogene (PIM2), mRNA7.1222 2982 + 2700 proteoglycan 1, secretory granule (PRG1 7.375 2983 +2640 mRNA for KIAA0870 protein, partial cds 7.375 2984 2723 c- EST, 5end /clone = IMAGE 7.375 2658 FYN-binding protein (FYB-120/130) (FYB)7.375 2985 2688 major histocompatibility complex, class 7.375 2986 2735c- EST, 3 end/clone = IMAGE: 7.375 2702 c- hypothetical protein MGC47077.634 2681 hypothetical protein FLJ10652 8.1117 2987 2755 EST, 3 end8.1117 2715 hypothetical protein FLJ10842 8.1117 2732 c-EST cDNA, 3 end8.1117 2652 hexokinase 2 (HK2), mRNA 8.1117 2651 colony stimulatingfactor 3 receptor 8.1117 2988 2718 RNA binding motif protein, X chrom8.2788 2673 Src-like-adapter (SLA), mRNA 8.3048 2989 2733 c- majorhistocompatibility complex 8.467 2712 histamine receptor H2 (HRH2)8.8583 2990 2659 hemopoietic cell kinase (HCK) 8.8583 2991 2654 xanthenedehydrogenase (XDH) 8.8583 2992 2636 Arabidopsis root cap 1 8.8583 29932639 fatty acid binding protein 1, liver 8.8583 2690 adenosine deaminase(ADA) 8.8583 2994 2705 c- EST, 3 end 8.8583 2995 2685 hypotheticalprotein MGC10823 8.8583 2996 2692 membrane-spanning 4-domains, 8.85832997 2693 rearranged immunoglobulin mRNA for mu 8.8583 + 2648 proteintyrosine kinase related mRNA 8.8583 2650 major histocompatibilitycomplex, class 8.8583 2998 2720 c- EST 3 end/clone = IMAGE: 8.8583 2660major histocompatibility complex, class 8.8583 2999 2666 BCL2-relatedprotein A1 (BCL2A1), mRNA 9.1446 3000 2699 c-EST 9.4767 2633 interleukin4 receptor 9.4767 3001 74 tumor necrosis factor (ligand) superfam 9.47673002 2672 interferon-induced, hepatitis C-assoc. 9.4767 3003 2642 cDNAFLJ20673 fis, clone KAIA4464 9.4767 3004 2682 VNN3 protein (HSA238982),mRNA 9.4767 3005 2655 cathepsin K (pycnodysostosis) (CTSK) 9.4767 30062630 Integrin, alpha L (CD11A (p180), lymphoc 9.4767 3007 2745 EST, 5end 9.4885 3008 2643 nuclear receptor subfamily 1, group I, 9.625 2694CDW52 antigen (CAMPATH-1) 9.625 3009 2749 6977, c-178F5 9.6903 3010 2665small inducible cytokine subfamily A 9.6903 3011 2649 signal transducerand activator 9.7878 3012 2637 324, 9.7878 2634 70 activation (Act-2)mRNA 9.7878 3013 2709 coagulation factor VII 9.7878 3014 2653 integrin,beta 2 (antigen CD18 (p95) 9.7878 3015 2729 EST 3′ end 9.8321

TABLE 9 Array RefSeq Current Probe mRNA Peptide UniGene SEQ AccessionAccession Cluster ID Gene Gene Name # # (Build 156) LocalizationFunction 111 IL15 Interleukin 15 NM_000585 NP_000576 Hs.168132 SecretedT-cell activation and proliferation 79 PRF1 Perform 1 (pore NM_005041NP_005032 Hs.2200 Secreted CD8, CTL effector; forming protein)channel-forming protein capable of lysing non- specifically a variety oftarget cells; clearance of virally infected host cells and tumor cells;110 IL17 Interleukin 17 NM_002190 NP_002181 Hs.41724 Secreted Inducesstromal cells to (cytotoxic T- produce proinflammatory lymphocyte- andhematopoietic cytokines; associated serine enhances IL6, IL8 andesterase 8) ICAM-1 expression in fibroblasts; osteoclastic boneresorption in RA; expressed in only in activated CD4 + T cells 75 IL8Interleukin 8 NM_000584 NP_000575 Hs.624 Secreted Proinflammatorycytokine 120 CXCL1 Chemokine (C-X- NM_001511 NP_001502 Hs.789 SecretedNeurogenesis, immune C motif) ligand 1 system development, (melanomagrowth signaling stimulating activity, alpha) 113 IFNG Interferon, gammaNM_000619 NP_000610 Hs.856 Secreted Antiviral defense and immuneactivation 100 IL2 Interleukin 2 NM_000586 NP_000577 Hs.89679 SecretedPromotes growth of B and T cells 4 B2M beta 2 NM_004048 NP_004039Hs.75415 Secreted microglobulin 98 CCL5 Chemokine (C-C NM_002985NP_002976 Hs.241392 Secreted Chemoattractant for motif) ligand 5monocytes, memory T (RANTES, helper cells and SCYAS) eosinophils; causesrelease of histamine from basophils and activates eosinophils; One ofthe major HIV- suppressive factors produced by CD8+ cells 112 IL10Interleukin 10 NM_000572 NP_000563 Hs.193717 Secreted Chemotactic factorfor CD8 + T cells; down- regulates expression of Th1 cytokines, MHCclass II Ags, and costimulatory molecules on macrophages; enhances Bcell survival, proliferation, and antibody production; blocks NF kappaB, JAK-STAT regulation; 80 IL4 Interleukin 4 NM_000589 NP_000580Hs.73917 Secreted TH2, cytokine, stimulates CTL 2773 IL7 Interleukin 7NM_000880 NP_000871 Hs.72927 Secreted Proliferation of lymphoidprogenitors 109 CXCL10 Chemokine (C-X- NM_001565 NP_001556 Hs.2248Secreted Stimulation of C motif) ligand 10, monocytes, NK and T SCYB10cell migration, modulation of adhesion molecule expression 2665 CCL17Chemokine (C-C NM_002987 NP_002978 Hs.66742 Secreted T cell development,motif) ligand 17 trafficking and activation 101 KLRF1 Killer celllectin- NM_016523 NP_057607 Hs.183125 Secreted Induction of IgE, IgG4,like receptor CD23, CD72, surface subfamily F, IgM, and class II MHCmember 1 antigen in B cells 99 IL6 Interleukin 6 NM_000600 NP_000591Hs.93913 Secreted B cell maturation 104 CCL4 Chemokine (C-C NM_002984NP_002975 Hs.75703 Secreted Inflammatory and motif) ligand 4chemokinetic properties; one of the major HIV- suppressive factorsproduced by CD8 + T cells 76 GZMB Granzyme B NM_004131 NP_004122 Hs.1051Secreted Apoptosis; CD8, CTL (granzyme 2, effector cytotoxic T-lymphocyte- associated serine esterase 1) 2785 OID_4789 KIAA0963 proteinNM_014963 NP_055778 Hs.7724 Secreted Proinflammatory; chemoattractionand activation of neutrophils 2791 XCL1 Chemokine (C NM_002995 NP_002986Hs.3195 Secreted Chemotactic factor for motif) ligand 1 lymphocytes butnot (SCYC2) monocytes or neutrophils 130 PRDM1 PR domain NM_001198NP_001189 Hs.388346 Nuclear Transcription factor; containing 1, promotesB cell with ZNF maturation, represses domain human beta-IFN geneexpression 2781 TBX21 T-box 21 NM_013351 NP_037483 Hs.272409 Nuclear TH1differentiation, transcription factor 88 MTHFD2 Methylene NM_006636NP_006627 Hs.154672 Mitochondrial Folate metabolism tetrahydrofolatedehydrogenase (NAD + dependent), methenyltetra- hydrofolatecyclohydrolase 103 IL2RA Interleukin 2 NM_000417 NP_000408 Hs.1724Membrane- T cell mediated immune receptor, alpha bound and responsesoluble forms 77 TNFSF6 Tumor necrosis NM_000639 NP_000630 Hs.2007Membrane- CD8, CTL effector; factor (ligand) bound and proapoptoticsuperfamily, soluble forms member 6 115 CD8B1 CD8 antigen, betaNM_004931 NP_004922 Hs.2299 Membrane- CTL mediated killing polypeptide 1bound and (p37) soluble forms 128 PTGS2 Prostaglandin- NM_000963NP_000954 Hs.196384 Membrane- Angiogenesis, cell endoperoxide associatedmigration, synthesis of synthase 2 inflammatory (prostaglandinprostaglandins G/H synthase and cyclooxygenase) 89 TAP1 Transporter 1,NM_000593 NP_000584 Hs.352018 ER membrane Transports antigens intoATP-binding ER for association with cassette, sub- MHC class I moleculesfamily B (MDR1/TAP) 92 IGHM Immunoglobulin BC032249 Hs.300697Cytoplasmic Antibody subunit heavy constant mu and secreted forms 122GP1 Glucose phosphate NM_000175 NP_000166 Hs.409162 CytoplasmicGlycolysis and isomerase and secreted gluconeogenesis forms(cytoplasmic); neurotrophic factor (secreted) 2783 GSN GelsolinNM_000177 NP_000168 Hs.290070 Cytoplasmic Controls actin filament(amyloidosis, and secreted assembly/disassembly Finnish type) forms 2780STK39 Serine threonine NM_013233 NP_037365 Hs.199263 CytoplasmicMediator of stress- kinase 39 and nuclear activated signals; (STE20/SPS1Serine/Thr Kinase, homolog, yeast) activated p38 2770 PSMB8 ProteasomeAK092738 Hs.180062 Cytoplasmic Processing of MHC class (prosome, Iantigens macropain) subunit, beta type, 8 (large multifunctionalprotease 7) 2667 LPXN Leupaxin NM_004811 NP_004802 Hs.49587 CytoplasmicSignal transduction 2669 ITK IL2-inducible T- L10717 Hs.211576Cytoplasmic Intracellular kinase, T- cell kinase cell proliferation anddifferentiation 90 KPNA6 Karyopherin alpha AW021037 Hs.301553Cytoplasmic Nucleocytoplasmic 6 (importin alpha transport 2794 SH2D2ASH2 domain NM_003975 NP_003966 Hs.103527 Cytoplasmic CD8 T activation,signal protein 2A transduction 2765 TNFSF5 Tumor necrosis NM_000074NP_000065 Hs.652 Cellular B-cell proliferation, IgE factor (ligand)membrane production, superfamily, immunoglobulin class member 5 (hyper-switching; expressed on IgM syndrome) CD4 + and CD8 + T cells 97 CD69CD69 antigen NM_001781 NP_001772 Hs.82401 Cellular Activation of (p60,early T-cell membrane lymphocytes, activation antigen) monocytes, andplatelets 2635 IL2RG Interleukin 2 NM_000206 NP_000197 Hs.84 CellularSignalling component of receptor, gamma membrane many interleukin(severe combined receptors immunodeficiency) (IL2, IL4, IL7, IL9, andIL15), 96 CXCR4 Chemokine (C-X- NM_003467 NP_003458 Hs.89414 CellularB-cell lymphopoiesis, C motif) receptor membrane leukocyte migration, 4angiogenesis; mediates intracellular calcium flux 2766 CD19 CD19 antigenNM_001770 NP_001761 Hs.96023 Cellular Signal transduction; B membranelymphocyte development, activation, and differentiation 2769 ITGB1Integrin, beta 1 NM_002211 NP_002202 Hs.287797 Cellular Cell-cell andcell-matrix (fibronectin membrane interactions receptor, betapolypeptide, antigen CD29 includes MDF2, MSK12) 2647 TRB T cell receptorK02885 Hs.300697 Cellular Antigen recognition beta, constant membraneregion 82 CTLA4 Cytotoxic T- NM_005214 NP_005205 Hs.247824 CellularNegative regulation of T lymphocyte- membrane cell activation, expressedassociated by activated T cells protein 4 83 CD8A CD8 antigen, NM_001768NP_001759 Hs.85258 Cellular CD8 T-cell specific alpha polypeptidemembrane marker and class I MHC (p32) receptor 114 HLA-DRB1 MajorNM_002124 NP_002115 Hs.308026 Cellular Antigen presentationhistocompatibility membrane complex, class II, DR beta 1 2772 CD3Z CD3Zantigen, NM_000734 NP_000725 Hs.97087 Cellular T-cell marker; coupleszeta polypeptide membrane antigen recognition to (TiT3 complex) severalintracellular signal-transduction pathways 2 ACTB Actin, beta NM_001101NP_001092 Hs.288061 Cellular Cell adhesion and membrane recognition 2774ITGAL Integrin, alpha L NM_002209 NP_002200 Hs.174103 Cellular Allleukocytes; cell-cell (antigen CD11A membrane adhesion, signaling(p180), lymphocyte function- associated antigen 1; alpha polypeptide) 78TCIRG1 T-ceIl, immune NM_006019 NP_006010 Hs.46465 Cellular T cellactivation regulator 1, membrane ATPase, H+ transporting, lysosomal V0protein a isoform 3 2670 CD72 CD72 antigen NM_001782 NP_001773 Hs.116481Cellular B cell proliferation membrane 2779 D12S2489E DNA segment onNM_007360 NP_031386 Hs.74085 Cellular NK cells marker chromosome 12membrane (unique) 2489 expressed sequence 2692 MS4A1 Membrane- NM_152866NP_690605 Hs.89751 Cellular B-cell activation, plasma spanning 4-membrane cell development domains, subfamily A, member 1, CD20 126TCRGC2 T cell receptor M17323 Hs.112259 Cellular gamma constant 2membrane 116 CD4 CD4 antigen (p55) NM_000616 NP_000607 Hs.17483 CellularT cell activation, signal membrane transduction, T-B cell adhesion 117CXCR3 Chemokine (C-X- NM_001504 NP_001495 Hs.198252 Cellular Integrinactivation, C motif) receptor membrane cytoskeletal changes and 3, GPR9chemotactic migration of leukocytes 2707 CD33 CD33 antigen NM_001772NP_001763 Hs.83731 Cellular Cell adhesion; receptor (gp67) membrane thatinhibits the proliferation of normal and leukemic myeloid cells 123 CD47CD47 antigen (Rh- NM_001777 NP_001768 Hs.82685 Cellular Cell adhesion,related antigen, membrane membrane transport, integrin-associatedsignaling transduction, signal transducer) permeability 84 BY55 Naturalkiller cell NM_007053 NP_008984 Hs.81743 Cellular NK cells and CTLs,receptor, membrane costim with MHC I immunoglobulin superfamily member2784 KLRD1 Killer cell lectin- NM_002262 NP_002253 Hs.41682 Cellular NKcell regulation like receptor membrane subfamily D, member 1 124 HLA-FMajor NM_018950 NP_061823 Hs.377850 Cellular Antigen presentationhistocompatibility membrane complex, class I, F 2752 PTPRCAP Proteintyrosine NM_005608 NP_006599 Hs.155975 Cellular T cell activationphosphatase, membrane receptor type, C- associated protein

TABLE 12 Markers for CMV Infection New SAM SEQID Source Unigene Acc GIName Strand Probe Sequence FDR 408 cDNA Hs.1051 NM_004131 7262379granzyme B 1 GGAGCCAAGTCCAGATT   0% TACACTGGGAGAGGTGC CAGCAACTGAATAAAT3108 db Hs.169824 NM_002258 4504878 killer cell lectin- 1TGGATCTGCCAAAAAGA   0% mining like receptor ACTAACACCTGTGAGAAATAAAGTGTATCCTGA 3109 cDNA Hs.170019 NM_004350 4757917 runt-related 1GCTGGGTGGAAACTGCT   0% transcription TTGCACTATCGTTTGCT factor 3TGGTGTTTGTTTTTAA 433 cDNA Hs.183125 NM_016523 7705573 killer celllectin- 1 TTCCAGGCTTTTGCTAC   0% like receptor F TCTTCACTCAGCTACAATAAACATCCTGAATGT 3110 db Hs.2014 X06557 37003 T-cell receptor- 1GGGGTTTATGTCCTAAC 0.10% mining delta TGCTTTGTATGCTGTTT TATAAAGGGATAGAAG3111 cDNA Hs.211535 AI823649 5444320 EST IMAGE: −1 GAAGCCTTTTCTTTTCT0.10% 2400148 GTTCACCCTCACCAAGA GCACAACTTAAATAGG 3112 cDNA Hs.301704AW002985 5849991 eomesodermin −1 AACAAGCCATGTTTGCC   0% (XenopusCTAGTCCAGGATTGCCT laevis) CACTTGAGACTTGCTA 3112 Table 3B Hs.301704AW002985 5849991 eomesodermin −1 AACAAGCCATGTTTGCC   0% (XenopusCTAGTCCAGGATTGCCT laevis) CACTTGAGACTTGCTA 3113 cDNA Hs.318885 NM_00063610835186 superoxide 1 TACTTTGGGGACTTGTA 0.10% dismutase 2GGGATGCCTTTCTAGTC CTATTCTATTGCAGTT 3114 literature Hs.41682 NM_0073347669498 killer cell lectin- 1 GGGCAGAGAAGGTGGAG   0% like receptor DAGTAAAGACCCAACATT ACTAACAATGATACAG 3115 cDNA Hs.71245 AI954499 5746809EST IMAGE: −1 TGGTAATAGTGTTTGAC   0% 502221 TCCAGGGAAGAACAGATGGGTGCCAGAGTGAAA 3116 cDNA Hs.75596 NM_000878 4504664 interleukin 2 1ATGGAAATTGTATTTGC   0% receptor, beta CTTCTCCACTTTGGGAG GCTCCCACTTCTTGGG436 cDNA Hs.75703 NM_002984 4506844 small inducible 1 CCACTGTCACTGTTTCT  0% cytokine A4 CTGCTGTTGCAAATACA TGGATAACACATTTGA 436 cDNA Hs.75703NM_002984 4506844 small inducible 1 CCACTGTCACTGTTTCT 0.10% cytokine A4CTGCTGTTGCAAATACA TGGATAACACATTTGA 436 cDNA Hs.75703 NM_002984 4506844small inducible 1 GTCCACTGTCACTGTTT   0% cytokine A4 CTCTGCTGTTGCAAATACATGGATAACACATTT 436 cDNA Hs.75703 NM_002984 4506844 small inducible −1TGGTCCACTGTCACTGT 0.10% cytokine A4 TTCTCTGCTGTTGCAAA TACATGGATAACACAT415 cDNA Hs.85258 BC025715 19344021 CD8 antigen 1 CTGAGAGCCCAAACTGC0.10% TGTCCCAAACATGCACT TCCTTGCTTAAGGTAT 3117 cDNA Hs.111554 AA8062222874972 cDNA 196D7 −1 TGATTTCTGTAATGTTT   0% GACCTAATAATAGCCCTTTTCGTCTCTGACCCA WBC N/A N/A N/A N/A N/A N/A 0.10% WPT N/A N/A N/A N/AN/A N/A   0%

1. A method of assessing the immune status of an individual comprisingdetecting the expression level of one or more genes expressed atdifferent levels depending upon the rate of hematopoiesis or thedistribution of hematopoietic cells along their maturation pathway insaid individual, wherein said one or more genes comprise the nucleotidesequence of SEQ ID NO:
 52. 2. The method of claim 1 wherein saidexpression level is detected by measuring the RNA level expressed bysaid one or more genes.
 3. The method of claim 2, further includingisolating RNA from said patient prior to detecting said RNA levelexpressed by said one or more genes.
 4. The method of claim 2 whereinsaid RNA level is detected by PCR.
 5. The method of claim 2 wherein saidRNA level is detected by hybridization.
 6. The method of claim 2 whereinsaid RNA level is detected by hybridization to an oligonucleotide. 7.The method of claim 6 wherein said oligonucleotide comprises DNA, RNA,cDNA, PNA, genomic DNA, or synthetic oligonucleotides.